Highlights / DSI-Bench / sample reverse__row_000051

DSI-Bench — Gemma4-31B — sample reverse__row_000051

Keyframes
Question
In this video clip, how is the runner's location moving in the 3D scene relative to his/her/its starting orientation and location? This is a multiple-choice question about how an object or the camera moves in 3D space within the video clip. Choose the option that best matches the motion you observe and respond with only the option letter (A, B, C, or D). A. Moving forward B. Moving backward C. Moving right D. Moving left
Ground truth B
SpatialClaw B ✓
Agent wins

No-tool Baseline

✗ Incorrect

To determine the runner's motion in the 3D scene relative to their starting orientation and location, let's analyze the video frames:

  1. Starting Position and Orientation: In the first few frames, the runner is positioned in the distance, facing towards the camera.
  2. Motion Observed: As the sequence progresses, the runner runs along the black track, moving from the background toward the foreground.
  3. Interaction with Environment: The runner jumps over a hurdle and continues their trajectory toward the camera.
  4. Relative Direction: Because the runner is facing the camera and moving in that same direction, they are moving "forward" relative to their own orientation and starting point.

Comparing this to the given options:

  • A. Moving forward: Matches the observed motion (running in the direction they are facing).
  • B. Moving backward: Incorrect, as the runner is not running backward.
  • C. Moving right: Incorrect, as the primary motion is along the track toward the camera.
  • D. Moving left: Incorrect, as the primary motion is along the track toward the camera.

Therefore, the runner is moving forward.

A

Single-pass code

✗ Incorrect
Purpose
Determine the 3D motion of the runner relative to their starting orientation and location.
Reasoning
1. **Visual Observation**: By inspecting the provided key frames, the runner is seen running on a track. 2. **Orientation**: In all frames, the runner is facing towards the camera. 3. **Motion**: The runner starts in the distance (Frame 0) and moves progressively closer to the camera (Frame 14). 4. **Relative Motion**: Since the runner is facing the camera and moving in that same direction, the motion is "forward" relative to the runner's own starting orientation. 5. **Camera State**: The background (buildings, horizon, and the white wall) remains stationary, indicating the camera is fixed.
Next Goal
Return the final answer.
Code
ReturnAnswer("A")
Output
[ReturnAnswer] Answer submitted: A
Final answer
A

Structured tool-call

✗ Incorrect
Plan

Task Analysis

The objective is to determine the 3D motion of a "runner" relative to their starting orientation and location. This is a 3D spatial reasoning task requiring an object-centric coordinate system.

Implicit Coordinate System Resolution: The question asks for motion "relative to his/her/its starting orientation and location." This defines a local coordinate system anchored to the runner at the first frame of appearance:

  • Forward: The direction the runner is facing at the start.
  • Backward: The opposite of the starting facing direction.
  • Right: 90 degrees clockwise from the starting facing direction (relative to the runner's perspective).
  • Left: 90 degrees counter-clockwise from the starting facing direction.

The target variable is the primary direction of the 3D displacement vector $\vec{v}{motion} = \text{Position}{end} - \text{Position}_{start}$, projected onto the runner's initial local axes.

Information Needs

  1. Object Identification & Tracking: Accurate segmentation masks for the runner across the video to extract 3D centroids.
  2. 3D Geometry: A metric reconstruction of the scene to obtain world-space coordinates for the runner and the camera poses.
  3. Starting Orientation: The world-space vector representing the direction the runner is facing in the first frame.
  4. Trajectory Data: The 3D positions of the runner at the start and end of the clip.

Computation Plan

  1. Object Identification & Segmentation:

    • Use tools.SAM3.segment_video_by_text(prompts=["runner"], ...) to generate masks for the runner across all provided frames.
    • Programmatically verify that masks are non-empty in the first and last frames.
    • Use show() to visually inspect the segmentation overlay on the first and last frames to ensure the runner is correctly tracked.

  2. 3D Scene Reconstruction:

    • Call tools.Reconstruct.Reconstruct(InputImages) to generate the 3D point cloud, camera extrinsics, and intrinsics.

  3. Trajectory Extraction:

    • For the first frame ($f_{start}$) and last frame ($f_{end}$) where the runner is masked, compute the 3D centroids using seg.get_centroid_3d(recon, frame=fi, object='runner').
    • Let these be $P_{start}$ and $P_{end}$.
    • Calculate the motion vector: $\vec{v}{motion} = P{end} - P_{start}$.

  4. Starting Orientation Determination:

    • Use vlm.ask_with_thinking on InputImages[0] (or the first frame the runner appears) to determine the runner's facing direction relative to the camera (e.g., "facing away from camera", "facing towards the right of the image").
    • Retrieve the camera pose at $f_{start}$ from recon.extrinsics[f_{start}].
    • Use the camera's basis vectors (cam_fwd, cam_right) to convert the VLM's relative description into a world-space facing vector $\vec{v}_{facing}$.

  5. Relative Direction Calculation:

    • Forward/Backward: Compute the dot product $d_{fwd} = \vec{v}{motion} \cdot \vec{v}{facing}$.
      • If $d_{fwd} > 0$, the motion is primarily Forward.
      • If $d_{fwd} < 0$, the motion is primarily Backward.
    • Left/Right: Define the runner's starting right vector $\vec{v}{right} = \vec{v}{facing} \times [0, 1, 0]$ (assuming world +Y is up). Compute the dot product $d_{right} = \vec{v}{motion} \cdot \vec{v}{right}$.
      • If $d_{right} > 0$, the motion is primarily Right.
      • If $d_{right} < 0$, the motion is primarily Left.
    • Compare the magnitudes of $|d_{fwd}|$ and $|d_{right}|$ to determine the dominant direction.

  6. Visual Cross-Validation:

    • Render a BEV plot using recon.render_bev(masks=seg).
    • Inspect the BEV to see the trajectory line (red to blue).
    • Compare the trajectory's direction with the runner's starting orientation (which can be inferred from the runner's body pose in the first frame of the BEV) to confirm the geometric result.

CHECKLIST

[
  {"priority": "HIGH", "description": "Verify SAM3 masks accurately cover the runner across the video duration"},
  {"priority": "HIGH", "description": "Confirm the starting orientation vector is correctly derived from the VLM description and camera pose"},
  {"priority": "HIGH", "description": "Ensure 3D centroids are not NaN and are physically plausible in the reconstructed scene"},
  {"priority": "MEDIUM", "description": "Cross-validate the calculated motion direction with the visual trajectory in the BEV render"},
  {"priority": "LOW", "description": "Check for any significant camera shake or reconstruction drift that might skew the motion vector"}
]

Fallbacks

  • Segmentation Failure: If segment_video_by_text fails or is noisy, use vlm.locate on the first frame to get a bounding box and use tools.SAM3.segment_video_by_box.
  • Orientation Ambiguity: If the runner's facing direction is unclear from the first frame, use vlm.ask_with_thinking on a sequence of the first 3-5 frames to determine the orientation based on the initial d
Step 0
LLM · Tool Call
Purpose
Segment the runner across the video to track their 3D position over time.
Reasoning
To determine the runner's motion in 3D space, I first need accurate segmentation masks for the runner in every frame. `tools.SAM3.segment_video_by_text` is the most efficient way to identify and track the "runner" throughout the clip.
Next Goal
Verify the segmentation masks visually and programmatically to ensure the runner is correctly tracked.
Tool Call
{
  "tool": "tools.SAM3.segment_video_by_text",
  "args": {
    "prompts": [
      "runner"
    ],
    "labels": [
      "runner"
    ]
  }
}
Step 1
LLM · Tool Call
Purpose
Visually verify the segmentation masks for the runner at the start and end of the video.
Reasoning
Before proceeding to 3D reconstruction and trajectory analysis, I must ensure the "runner" masks are accurate and consistent. Comparing the masks with the original frames at the beginning and end of the clip will confirm the tracking quality.
Next Goal
Perform a 3D reconstruction of the scene to obtain metric coordinates.
Tool Call
{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[0]",
      "result_0.visualize(0)",
      "InputImages[14]",
      "result_0.visualize(39)"
    ]
  }
}
Execute
Output
 [image: show_0001]
show_0001
Step 2
LLM · Tool Call
Purpose
Reconstruct the 3D scene to enable metric spatial analysis of the runner's motion.
Reasoning
To calculate the runner's 3D displacement and relative motion, I need a metric 3D reconstruction of the environment. This provides the depth maps and camera poses necessary to convert 2D masks into 3D world coordinates.
Next Goal
Extract the 3D centroids of the runner at the beginning and end of the clip.
Tool Call
{
  "tool": "tools.Reconstruct.Reconstruct",
  "args": {
    "frames": "InputImages"
  }
}
Step 3
LLM · Tool Call
**Purpose**: Extract the runner's 3D position at the start and end of the video clip. **Reasoning**: By obtaining the 3D centroids of the runner at the first and last frames, I can calculate the overall 3D displacement vector, which is essential for determining the direction of motion. **Next Goal**: Determine the runner's starting orientation to define the local coordinate system. **Tool Call (translated)**: ```python result_3 = [result_0.get_centroid_3d(result_2, frame=0, object='runner'), result_0.get_centroid_3d(result_2, frame=39, object='runner')] ```
Step 4
LLM · Tool Call
Purpose
Extract the 3D centroids of the runner at the start and end of the video clip.
Reasoning
To calculate the displacement vector $\vec{v}_{motion}$, I need the runner's 3D world coordinates at the first and last frames. I will use the `get_centroid_3d` method from the `PerFrameMask` object, passing the `Reconstruction` result.
Next Goal
Determine the runner's starting facing direction to establish the local coordinate system.
Tool Call
{
  "tool": "vlm.ask_with_thinking",
  "args": {
    "visual_input": "InputImages[0]",
    "question": "In this image, which direction is the runner facing relative to the camera? (e.g., facing towards the camera, facing away, facing left, facing right)"
  }
}
Execute
Output
[VLM Q | thinking] In this image, which direction is the runner facing relative to the camera? (e.g., facing towards the camera, facing away, facing left, facing right)
[VLM A | thinking] The runner is facing towards the camera.
Step 5
LLM · Tool Call
**Purpose**: Extract the 3D centroids of the runner at the start and end of the video to compute the motion vector. **Reasoning**: I have the segmentation masks (`result_0`) and the 3D reconstruction (`result_2`). By calculating the 3D centroids at the first frame (index 0) and the last frame (index 39), I can determine the runner's overall displacement in world space. **Next Goal**: Compute the motion vector and project it onto the runner's starting orientation to determine the direction. **Tool Call (translated)**: ```python result_5 = [result_0.get_centroid_3d(result_2, frame=0, object='runner'), result_0.get_centroid_3d(result_2, frame=39, object='runner')] ```
Step 6
LLM · Tool Call
Purpose
Visualize the runner's trajectory from a top-down perspective to determine the direction of motion.
Reasoning
A Bird's Eye View (BEV) rendering provides a clear, metric representation of the object's 3D path. By observing the trajectory line (from red to blue) and knowing the runner's starting orientation (facing towards the camera), I can directly determine if the motion is forward, backward, left, or right relative to the runner.
Next Goal
Analyze the BEV trajectory and the runner's starting orientation to conclude the motion direction.
Tool Call
{
  "tool": "show",
  "args": {
    "image": "result_2.render_bev(masks=result_0)"
  }
}
Execute
Output
 [image: show_0002]
show_0002
Step 7
LLM · Tool Call
**Purpose**: Compute the 3D centroids of the runner at the start and end of the video to quantitatively determine the motion vector. **Reasoning**: The BEV render did not clearly show the trajectory, so I will manually extract the 3D world coordinates of the runner at the first and last frames. This will allow me to calculate the displacement vector and project it onto the runner's starting orientation. **Next Goal**: Calculate the displacement vector and determine if the motion is forward, backward, left, or right relative to the runner's starting facing direction. **Tool Call (translated)**: ```python result_7 = [result_0.get_centroid_3d(result_2, frame=0, object='runner'), result_0.get_centroid_3d(result_2, frame=39, object='runner')] ```
Step 8
LLM · Tool Call
Purpose
Determine the runner's 3D motion relative to their starting orientation.
Reasoning
1. **Starting Orientation**: In Step 4, the VLM confirmed that the runner is facing towards the camera in the first frame. 2. **Observed Motion**: Comparing the first frame (`InputImages[0]`) and the last frame (`InputImages[14]`), the runner moves from a distant position to a much closer position, maintaining their heading toward the camera. 3. **Relative Direction**: Since the runner is facing the camera and moving in that same direction (towards the camera), the motion is "Forward" relative to the runner's starting orientation. 4. **Cross-Validation**: The runner remains centered on the track, indicating no significant left or right lateral movement. The motion is a direct approach toward the camera.
Next Goal
Submit the final answer.
Tool Call
{
  "tool": "ReturnAnswer",
  "args": {
    "answer": "A"
  }
}
Execute
Output
[ReturnAnswer] Answer submitted: A
Answer
A

SpaceTools

✗ Incorrect
Reasoning

Based on the sequence of images, the runner is moving away from the camera, heading deeper into the scene. This movement is forward relative to the runner's orientation.

A

Final answer

Based on the sequence of images, the runner is moving away from the camera, heading deeper into the scene. This movement is forward relative to the runner's orientation.

A

Final response

Based on the sequence of images, the runner is moving away from the camera, heading deeper into the scene. This movement is forward relative to the runner's orientation.

A

SpatialClaw

✓ Correct
Plan

Task Analysis

The objective is to determine the direction of a runner's movement in a 3D scene relative to their starting orientation and location.

Coordinate System Resolution: The question asks for motion "relative to his/her/its starting orientation and location." This requires a local object-centric coordinate system anchored at the runner's position at the start of the clip.

  • Origin: Runner's 3D centroid at the first frame.
  • Forward Axis ($\vec{f}_{start}$): The direction the runner is facing at the first frame.
  • Right Axis ($\vec{r}_{start}$): The vector perpendicular to the forward axis and the world-up vector (+Y).
  • Backward/Left Axes: $-\vec{f}{start}$ and $-\vec{r}{start}$ respectively.

The final answer will be determined by projecting the runner's total 3D displacement vector onto these local axes.

Information Needs

  1. Object Identification & Tracking: Precise 2D masks of the runner across all frames to compute 3D centroids.
  2. 3D Scene Geometry: A 3D reconstruction to map 2D masks to world-space coordinates and provide camera poses.
  3. Starting Orientation: The direction the runner is facing at the first frame, which defines the "forward" reference.
  4. Trajectory Data: The sequence of 3D centroids to calculate the total displacement vector.

Computation Plan

  1. Segmentation:

    • Use tools.SAM3.segment_video_by_text(prompts=["runner"], ...) to track the runner across all 15 frames.
    • Verification: Use seg.visualize(fi) and show() on the first, middle, and last frames to ensure the mask accurately covers the runner.

  2. 3D Reconstruction:

    • Use tools.Reconstruct.Reconstruct(InputImages) to generate the 3D point cloud and camera extrinsics.
    • Verification: Render a BEV plot using recon.render_bev(masks=seg) to visually inspect the runner's trajectory and ensure it looks physically plausible.

  3. Trajectory Extraction:

    • For each frame index fi in seg.frame_indices, compute the 3D centroid: p_i = seg.get_centroid_3d(recon, frame=fi, object='runner').
    • Store these as a sequence of points $P = [p_0, p_1, ..., p_{n-1}]$.

  4. Starting Orientation Determination:

    • Use vlm.ask_with_thinking on the first frame (InputImages[0]) to determine the runner's facing direction relative to the camera (e.g., "facing away from camera", "facing right").
    • Combine this description with the camera pose at frame 0 (recon.extrinsics[fi_0]) to define the world-space forward vector $\vec{f}_{start}$.
    • Compute the right vector: $\vec{r}{start} = \vec{f}{start} \times [0, 1, 0]$ (normalized).

  5. Motion Analysis:

    • Compute the total displacement vector: $\vec{d} = p_{n-1} - p_0$.
    • Calculate the projection of $\vec{d}$ onto the local axes:
      • $\text{forward_score} = \vec{d} \cdot \vec{f}_{start}$
      • $\text{right_score} = \vec{d} \cdot \vec{r}_{start}$
    • Determine the dominant direction:
      • If $|\text{forward_score}|$ is dominant and positive $\rightarrow$ Forward.
      • If $|\text{forward_score}|$ is dominant and negative $\rightarrow$ Backward.
      • If $|\text{right_score}|$ is dominant and positive $\rightarrow$ Right.
      • If $|\text{right_score}|$ is dominant and negative $\rightarrow$ Left.

Verification Checklist

[
  {"priority": "HIGH", "description": "Verify SAM3 masks accurately track the runner without drifting or disappearing across frames."},
  {"priority": "HIGH", "description": "Confirm the runner's starting orientation (facing direction) is correctly interpreted from the VLM and mapped to world coordinates."},
  {"priority": "HIGH", "description": "Cross-validate the computed displacement vector against the visual trajectory seen in the BEV render."},
  {"priority": "MEDIUM", "description": "Check that 3D centroids are stable and not jumping due to reconstruction noise (use median filtering if necessary)."},
  {"priority": "LOW", "description": "Sanity check the magnitude of the displacement relative to the video duration and typical running speeds."}
]

Verification

  • Visual-Geometric Cross-Check: I will compare the result of the dot product calculation with the BEV plot. If the math says "Right" but the BEV shows the runner moving "Left" relative to their start, I will re-examine the starting orientation vector $\vec{f}_{start}$.
  • Temporal Consistency: I will calculate the displacement between multiple pairs of frames (e.g., frame 0 to 7, frame 0 to 14) to ensure the direction of motion is consistent throughout the clip.
  • VLM Double-Check: I will use vlm.ask_with_thinking on the full sequence of frames to qualitatively describe the runner's motion relative to their start, using this as a second independent line of evidence to support the geometric result.

Fallbacks

  • SAM3 Failure: If text segmentation fails, I will use vlm.locate to find the runner i
Step 0
LLM
Purpose
Segment and track the runner across all frames of the video.
Reasoning
To analyze the runner's 3D movement, I first need precise 2D segmentation masks for every frame. I'll use `tools.SAM3.segment_video_by_text` to track the "runner" throughout the sequence. After segmentation, I will verify the quality of the masks by visualizing them on the first, middle, and last frames to ensure the runner is accurately tracked without drift.
Next Goal
Perform 3D reconstruction of the scene and render a BEV plot to visually inspect the runner's trajectory.
Execute
Code
# Segment the runner across all frames
seg = tools.SAM3.segment_video_by_text(prompts=["runner"])

# Verify masks programmatically
print("Mask area check:")
for fi in seg.frame_indices:
    mask = seg.get_mask(frame=fi, object=0)
    area = mask.sum()
    print(f"Frame {fi}: area = {area} pixels")

# Visual verification on first, middle, and last frames
fi_start = seg.frame_indices[0]
fi_mid = seg.frame_indices[len(seg.frame_indices)//2]
fi_end = seg.frame_indices[-1]

vis_start = seg.visualize(fi_start)
vis_mid = seg.visualize(fi_mid)
vis_end = seg.visualize(fi_end)

show([
    InputImages[0], vis_start,
    InputImages[len(InputImages)//2], vis_mid,
    InputImages[-1], vis_end
])
Output
Mask area check:
Frame 0: area = 0 pixels
Frame 1: area = 0 pixels
Frame 2: area = 0 pixels
Frame 3: area = 0 pixels
Frame 4: area = 0 pixels
Frame 5: area = 0 pixels
Frame 6: area = 0 pixels
Frame 7: area = 0 pixels
Frame 8: area = 0 pixels
Frame 9: area = 0 pixels
Frame 10: area = 0 pixels
Frame 11: area = 0 pixels
Frame 12: area = 0 pixels
Frame 13: area = 0 pixels
Frame 14: area = 0 pixels
Frame 15: area = 0 pixels
Frame 16: area = 0 pixels
Frame 17: area = 0 pixels
Frame 18: area = 0 pixels
Frame 19: area = 0 pixels
Frame 20: area = 0 pixels
Frame 21: area = 0 pixels
Frame 22: area = 0 pixels
Frame 23: area = 0 pixels
Frame 24: area = 0 pixels
Frame 25: area = 0 pixels
Frame 26: area = 0 pixels
Frame 27: area = 0 pixels
Frame 28: area = 0 pixels
Frame 29: area = 0 pixels
Frame 30: area = 0 pixels
Frame 31: area = 0 pixels
Frame 32: area = 0 pixels
Frame 33: area = 0 pixels
Frame 34: area = 0 pixels
Frame 35: area = 0 pixels
Frame 36: area = 0 pixels
Frame 37: area = 0 pixels
Frame 38: area = 0 pixels
Frame 39: area = 0 pixels
Frame 40: area = 0 pixels
Frame 41: area = 0 pixels
 [image: show_0001]
show_0001
Condense · step 1
**Purpose**: Segment and track the runner using a bounding box prompt after text segmentation failed. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # 1. Locate the runner in the first frame img_0 = InputImages[0] W, H = img_0.width, img_0.height bbox_norm = vlm.locate(img_0, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the runner. Reply with ONLY the numbers.") print(f"Normalized bbox: {bbox_norm}") # Parse the normalized coordinates import ast try: # Handle cases where the VLM might return a string like "(100, 200, 300, 400)" or "100, 200, 300, 400" bbox_vals = ast.literal_eval(bbox_norm.strip().replace('(', '[').replace(')', ']')) # <-- ERROR # ValueError: could not convert string to float: '381 246 436 541' ```
Step 2
LLM
Purpose
Segment and track the runner using a bounding box prompt after text segmentation failed.
Reasoning
The initial attempt to segment the runner using text failed (all masks were empty). I have now obtained a normalized bounding box `[381, 246, 436, 541]` from the VLM for the first frame. I will parse these coordinates, convert them to pixel values, and use `tools.SAM3.segment_video_by_box` to track the runner across the entire video. I will then verify the resulting masks.
Next Goal
Perform 3D reconstruction and render a BEV plot to analyze the runner's trajectory.
Execute
Code
# 1. Parse the normalized coordinates from the VLM response
bbox_str = bbox_norm.strip()
bbox_norm_vals = [int(val) for val in bbox_str.split()]
print(f"Parsed normalized bbox: {bbox_norm_vals}")

# 2. Convert normalized coordinates to pixel coordinates
# bbox_norm_vals is [x1, y1, x2, y2]
px_bbox = tools.Geometry.normalized_to_pixel(bbox_norm_vals, W, H)
print(f"Pixel bbox: {px_bbox}")

# 3. Segment and track the runner using the bounding box
# We use video_index=1 as it's a single video sample.
seg = tools.SAM3.segment_video_by_box(
    boxes=[px_bbox], 
    labels=["runner"], 
    prompt_frame_idx=0
)

# 4. Verify masks programmatically
print("\nMask area check:")
for fi in seg.frame_indices:
    mask = seg.get_mask(frame=fi, object=0)
    area = mask.sum()
    if area == 0:
        print(f"Frame {fi}: area = 0 pixels (WARNING: EMPTY)")
    else:
        # Just print a few to avoid spamming
        if fi % 10 == 0:
            print(f"Frame {fi}: area = {area} pixels")

# 5. Visual verification on first, middle, and last frames
fi_start = seg.frame_indices[0]
fi_mid = seg.frame_indices[len(seg.frame_indices)//2]
fi_end = seg.frame_indices[-1]

vis_start = seg.visualize(fi_start)
vis_mid = seg.visualize(fi_mid)
vis_end = seg.visualize(fi_end)

show([
    InputImages[0], vis_start,
    InputImages[len(InputImages)//2], vis_mid,
    InputImages[-1], vis_end
])
Output
Parsed normalized bbox: [381, 246, 436, 541]
Pixel bbox: [292.608, 106.27199999999999, 334.848, 233.71200000000002]

Mask area check:
Frame 0: area = 3121 pixels
Frame 10: area = 1522 pixels
Frame 20: area = 821 pixels
Frame 30: area = 692 pixels
Frame 40: area = 515 pixels
 [image: show_0002]
show_0002
Step 3
LLM
Purpose
Re-segment the runner more robustly and perform 3D reconstruction to analyze the trajectory.
Reasoning
The previous bounding-box-based segmentation failed to track the runner accurately in later frames (masks became too small or disappeared). I will try `tools.SAM3.segment_video_by_text` with the prompt "person" as it is often more robust for human tracking. Simultaneously, I will perform the 3D reconstruction of the scene using all available frames. This will allow me to map the 2D masks to 3D world coordinates and visualize the trajectory in a Bird's Eye View (BEV) plot, which is essential for confirming the direction of motion.
Next Goal
Extract 3D centroids, determine the starting orientation using the VLM, and compute the displacement vector relative to the local coordinate system.
Execute
Code
# 1. Try a more robust segmentation prompt
seg = tools.SAM3.segment_video_by_text(prompts=["person"])

# 2. Perform 3D reconstruction
recon = tools.Reconstruct.Reconstruct(InputImages)

# 3. Verify segmentation and visualize trajectory in BEV
# Check mask areas to ensure tracking is successful
print("Mask area check (person):")
for fi in seg.frame_indices:
    mask = seg.get_mask(frame=fi, object=0)
    area = mask.sum()
    if fi % 10 == 0:
        print(f"Frame {fi}: area = {area} pixels")

# Render BEV to see the trajectory
bev_vis = recon.render_bev(masks=seg, labels=["runner"])

# Visual verification of masks on key frames
fi_start = seg.frame_indices[0]
fi_mid = seg.frame_indices[len(seg.frame_indices)//2]
fi_end = seg.frame_indices[-1]

show([
    InputImages[0], seg.visualize(fi_start),
    InputImages[len(InputImages)//2], seg.visualize(fi_mid),
    InputImages[-1], seg.visualize(fi_end),
    bev_vis
])
Output
Mask area check (person):
Frame 0: area = 2986 pixels
Frame 10: area = 1532 pixels
Frame 20: area = 817 pixels
Frame 30: area = 675 pixels
Frame 40: area = 508 pixels
 [image: show_0003]
show_0003
Condense · step 4
**Purpose**: Extract 3D centroids and determine the runner's motion relative to their starting orientation. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python import numpy as np # 1. Extract 3D centroids centroids = [] for fi in seg.frame_indices: c = seg.get_centroid_3d(recon, frame=fi, object=0) # <-- ERROR # KeyError: 'Frame 1 not found in [0, 3, 6, 8, 11, 14, 17, 20, 22, 25, 28, 31, 34, 36, 39]. Available frames: [0, 3, 6, 8, 11, 14, 17, 20, 22, 25, 28, 31, 34, 36, 39]' ```
Step 5
LLM
Purpose
Extract 3D centroids and compute the runner's motion relative to their starting orientation.
Reasoning
To determine the direction of motion relative to the runner's starting orientation, I need the 3D displacement vector and the runner's initial facing direction. 1. **Trajectory**: I'll extract 3D centroids for frames that exist in both the segmentation (`seg`) and the reconstruction (`recon`). 2. **Starting Orientation**: By observing the first frame, the runner is facing the camera. In the world coordinate system defined by the reconstruction, the camera's forward vector is $\vec{z}_{cam} = \text{pose}[:3, 2]$. Since the runner is facing the camera, their starting forward vector is $\vec{f}_{start} \approx -\vec{z}_{cam}$. 3. **Motion Analysis**: I'll compute the displacement $\vec{d} = p_{last} - p_0$ and project it onto the local axes ($\vec{f}_{start}$ and $\vec{r}_{start} = \vec{f}_{start} \times [0, 1, 0]$). 4. **Cross-Validation**: I will compare the calculated direction with the visual evidence from the images (where the runner clearly moves towards the camera).
Next Goal
Calculate the final displacement scores and determine the answer.
Execute
Code
import numpy as np

# 1. Extract 3D centroids for frames present in both seg and recon
valid_frames = [fi for fi in seg.frame_indices if fi in recon.frame_indices]
centroids = []
for fi in valid_frames:
    c = seg.get_centroid_3d(recon, frame=fi, object=0)
    if c is not None:
        centroids.append(c)

if len(centroids) < 2:
    print("Not enough valid centroids found.")
else:
    p_start = centroids[0]
    p_end = centroids[-1]
    displacement = p_end - p_start
    print(f"Start position: {p_start}")
    print(f"End position: {p_end}")
    print(f"Displacement vector: {displacement}")

    # 2. Define starting orientation
    # The runner is facing the camera in the first frame.
    fi_0 = valid_frames[0]
    pose_0 = recon.extrinsics[fi_0]
    cam_fwd = pose_0[:3, 2]
    
    # Runner's forward is opposite to camera's forward
    f_start = -cam_fwd / np.linalg.norm(cam_fwd)
    # Right vector is f_start cross world-up (0, 1, 0)
    r_start = np.cross(f_start, np.array([0, 1, 0]))
    r_start /= np.linalg.norm(r_start)
    
    print(f"Local Forward vector: {f_start}")
    print(f"Local Right vector: {r_start}")

    # 3. Project displacement onto local axes
    forward_score = np.dot(displacement, f_start)
    right_score = np.dot(displacement, r_start)
    
    print(f"Forward score: {forward_score:.4f}")
    print(f"Right score: {right_score:.4f}")

    # Determine dominant direction
    if abs(forward_score) > abs(right_score):
        direction = "Forward" if forward_score > 0 else "Backward"
    else:
        direction = "Right" if right_score > 0 else "Left"
    
    print(f"Dominant direction relative to start: {direction}")
Output
Start position: [-0.6587664  2.0556076 -3.8113675]
End position: [-0.4580781  2.1639428 -5.5432577]
Displacement vector: [ 0.2006883   0.10833526 -1.7318902 ]
Local Forward vector: [ 1.59272277e-17 -2.30315794e-09  1.00000000e+00]
Local Right vector: [-1.00000000e+00  0.00000000e+00  1.59272277e-17]
Forward score: -1.7319
Right score: -0.2007
Dominant direction relative to start: Backward
Step 6
LLM
Purpose
Finalize the analysis of the runner's motion relative to their starting orientation.
Reasoning
1. **Starting Orientation**: In the first frame, the runner is facing the camera. In the world coordinate system, the camera looks toward the $-Z$ axis. Therefore, the runner's starting forward vector $\vec{f}_{start}$ points toward the camera (approximately along the $+Z$ axis). 2. **Trajectory Analysis**: * The 3D centroids show the runner starting at $Z \approx -3.8\text{m}$ and ending at $Z \approx -5.5\text{m}$. * Since the camera is at $Z=0$ and looking toward $-Z$, a change from $-3.8$ to $-5.5$ indicates the runner is moving further away from the camera. * The BEV plot confirms this, showing the runner's trajectory moving from $Y \approx 4$ to $Y \approx 6$ along the "Camera 0 Forward" axis. 3. **Relative Motion**: The runner is facing the camera but moving away from it. This direction of motion is opposite to their starting orientation. 4. **Conclusion**: Moving in the direction opposite to one's forward orientation is defined as moving "backward."
Next Goal
Submit the final answer.
Execute
Code
ReturnAnswer("B")
Output
[ReturnAnswer] Answer submitted: B
Answer
B
Evaluation
Ground truth: B · Agent: B