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Infer complete object structure despite occlusion using multi-camera video. Enables training models to predict hidden object appearance by combining temporal and spatial context from multiple viewpoints.
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| name | training-x-ray-vision-amodal |
| title | Training for X-Ray Vision: Amodal Segmentation and View-Invariant Object Representation |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2507.00339 |
| keywords | ["Amodal Segmentation","Multi-camera Video","Object Understanding","Dataset","Occlusion Handling"] |
| description | Infer complete object structure despite occlusion using multi-camera video. Enables training models to predict hidden object appearance by combining temporal and spatial context from multiple viewpoints. |
Understanding what lies behind occlusions is fundamental to perception. Humans effortlessly imagine the back of a chair even when it's blocked by a table. Current computer vision models struggle with this task—they lack datasets pairing temporal video sequences with multiple camera perspectives that provide consistent ground truth about what's hidden.
The MOVi-MC-AC dataset addresses this gap by providing the first large-scale multi-camera video amodal dataset. With 5.8 million object instances across 2,041 synthetic scenes, it enables training models to reason about complete object geometry and appearance despite occlusion, using both temporal motion cues and spatial information from multiple viewpoints.
Amodal reasoning means understanding the complete form of an object even when parts are invisible. Traditional datasets either provide single-view snapshots or sequence-based temporal data, but lack the rich supervision needed to learn robust occlusion handling. MOVi-MC-AC solves this by:
This combination teaches models to fuse spatial and temporal cues—leveraging one camera's view to infer another's occluded regions.
The dataset construction pipeline consists of these key components:
The design prioritizes realistic occlusion patterns (not random) by simulating object dynamics, ensuring models learn generalizable rather than trivial solutions.
This section shows how to construct and use amodal reasoning in multi-camera settings.
Step 1: Load and explore multi-camera amodal data
The following code demonstrates how to load multi-view amodal annotations and inspect occlusion patterns:
import numpy as np
from pathlib import Path
# Load a multi-camera sequence
scene_path = Path("movi_mc_ac_dataset/scene_0001")
cameras = {}
for camera_id in range(6):
cam_data = {
'rgb': np.load(scene_path / f"camera_{camera_id}_rgb.npy"),
'modal_mask': np.load(scene_path / f"camera_{camera_id}_modal_mask.npy"),
'amodal_mask': np.load(scene_path / f"camera_{camera_id}_amodal_mask.npy"),
'amodal_rgb': np.load(scene_path / f"camera_{camera_id}_amodal_rgb.npy"),
'visibility': np.load(scene_path / f"camera_{camera_id}_visibility.npy")
}
cameras[camera_id] = cam_data
# Identify occluded regions: where modal and amodal masks differ
camera_0 = cameras[0]
occluded_mask = (camera_0['modal_mask'] != camera_0['amodal_mask']).astype(float)
print(f"Occlusion rate: {occluded_mask.mean():.2%}")
This loads synchronized data from all six viewpoints and identifies which pixels are occluded.
Step 2: Create correspondence between cameras using visibility tracking
Models must learn which occluded regions in one camera can be revealed by other cameras. This code finds spatial correspondences across viewpoints:
import cv2
def find_multiview_correspondences(cameras, object_id, frame_id):
"""Find which camera best reveals occluded parts of an object."""
visibility_scores = {}
for cam_id, cam_data in cameras.items():
# Extract visibility of this object in this camera
vis_map = cam_data['visibility'][object_id]
# Higher visibility = more of this object is visible in this camera
visibility_scores[cam_id] = vis_map.mean()
# Rank cameras by how completely they show this object
ranked = sorted(visibility_scores.items(), key=lambda x: x[1], reverse=True)
best_camera = ranked[0][0]
return best_camera, visibility_scores
# Find which camera best reveals object #5
best_cam, scores = find_multiview_correspondences(cameras, object_id=5, frame_id=0)
print(f"Best camera for object 5: {best_cam} (visibility: {scores[best_cam]:.2%})")
This identifies which viewpoint provides the most complete view of occluded objects.
Step 3: Train an amodal prediction model using multi-camera context
Here's a simplified training loop that teaches models to predict occluded appearance using both temporal and spatial context:
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
class AmodalPredictor(nn.Module):
def __init__(self, num_cameras=6):
super().__init__()
self.temporal_encoder = nn.LSTM(input_size=2048, hidden_size=512, num_layers=2)
self.spatial_fusion = nn.MultiheadAttention(embed_dim=512, num_heads=8)
self.decoder = nn.Sequential(
nn.Linear(512, 1024),
nn.ReLU(),
nn.Linear(1024, 3) # Predict RGB
)
def forward(self, modal_sequence, camera_features):
"""
modal_sequence: (T, B, C, H, W) - visible content over time
camera_features: (B, 6, D) - spatial features from 6 cameras
"""
B, T, C, H, W = modal_sequence.shape
# Encode temporal dynamics
temporal_out, (h, c) = self.temporal_encoder(modal_sequence.reshape(T, B, -1))
# Shape: (T, B, 512)
# Fuse spatial information from other cameras
fused, _ = self.spatial_fusion(
temporal_out[-1:], # Query from final temporal state
camera_features.transpose(0, 1), # Key/Value from all cameras
camera_features.transpose(0, 1)
)
# Shape: (1, B, 512)
# Decode to RGB prediction for occluded regions
amodal_pred = self.decoder(fused.squeeze(0))
return amodal_pred
# Training setup
model = AmodalPredictor(num_cameras=6)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.L1Loss()
for epoch in range(10):
for batch in dataloader:
modal_seq = batch['modal_sequence'] # (T, B, 3, 256, 256)
camera_feats = batch['camera_features'] # (B, 6, 512)
amodal_rgb = batch['amodal_rgb'] # (B, 3, 256, 256)
pred = model(modal_seq, camera_feats)
loss = criterion(pred, amodal_rgb)
optimizer.zero_grad()
loss.backward()
optimizer.step()
This trains a model to predict unoccluded appearance by combining temporal patterns and spatial information from multiple cameras.
Step 4: Evaluate amodal completeness with standard metrics
Evaluation uses both reconstruction quality and segmentation metrics adapted to focus on occluded regions:
from skimage.metrics import structural_similarity as ssim
from lpips import LPIPS
def evaluate_amodal_prediction(pred_amodal, gt_amodal, occluded_mask, method='all'):
"""Evaluate prediction quality on all or just occluded regions."""
lpips_fn = LPIPS(net='alex')
if method == 'occluded_only':
# Only evaluate quality in regions that were actually hidden
pred = pred_amodal[occluded_mask > 0]
gt = gt_amodal[occluded_mask > 0]
else:
pred = pred_amodal
gt = gt_amodal
# Standard reconstruction metrics
psnr = -10 * np.log10(((pred - gt) ** 2).mean())
ssim_score = ssim(pred, gt, channel_axis=0)
lpips_score = lpips_fn(pred, gt).item()
return {'PSNR': psnr, 'SSIM': ssim_score, 'LPIPS': lpips_score}
results = evaluate_amodal_prediction(
pred_amodal=model_output,
gt_amodal=ground_truth,
occluded_mask=occlusion_mask,
method='occluded_only'
)
print(f"Occluded region prediction: PSNR={results['PSNR']:.2f}")
This computes standard metrics (PSNR, LPIPS, SSIM) both globally and specifically on regions that were actually occluded.
When to use MOVi-MC-AC:
When NOT to use:
Hyperparameters and Configuration:
| Parameter | Recommended | Notes |
|---|---|---|
| Sequence Length | 64 frames | Longer captures more temporal cues but increases memory |
| Number of Cameras | 4-6 | More cameras provide richer spatial coverage; 6 is dataset maximum |
| Feature Dimension | 512 | Balance between expressiveness and training speed |
| Temporal Encoder Layers | 2 | Deeper models capture longer-range dependencies but require more data |
| Optimization Learning Rate | 1e-4 | Standard for vision transformers; reduce if diverging |
| Batch Size | 32 | Limited by GPU memory; smaller batches increase noise in temporal signals |
Common Pitfalls:
Key Design Decisions: The dataset uses synthetic data to guarantee pixel-perfect occlusion ground truth. While this differs from real photos, it's necessary because measuring hidden object appearance is impossible from images alone. The multi-camera setup ensures that occlusions are "recoverable"—hidden regions are visible from other angles—teaching models useful geometry rather than hallucination.
Gao, P., Ge, Y., An, L., Xia, B., Huang, J., Ding, C., & Wu, A. (2025). Training for X-Ray Vision: Amodal Segmentation and View-Invariant Object Representation. arXiv preprint arXiv:2507.00339. https://arxiv.org/abs/2507.00339