[Paper] Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generation

Source: arXiv - 2512.03040v1

Overview

The paper introduces Video4Spatial, a novel framework that pushes video diffusion models beyond mere frame synthesis toward genuine visuospatial intelligence. By conditioning on pure video context—without depth maps, pose vectors, or other auxiliary signals—the system can understand and act upon spatial instructions, enabling tasks like camera‑pose navigation and object grounding directly from video streams.

Key Contributions

• Context‑only conditioning: Demonstrates that video diffusion models can infer 3D geometry and spatial relationships using only raw video frames as context.
• Two benchmark tasks:
  1. Scene navigation – the model follows natural‑language camera‑pose commands while preserving scene consistency.
  2. Object grounding – it localizes and plans toward target objects based on semantic instructions.
• End‑to‑end spatial reasoning: No separate depth or pose estimation modules; the diffusion model jointly plans, grounds, and generates the resulting video.
• Robust generalization: Works on longer video contexts and on out‑of‑domain environments unseen during training.
• Data curation pipeline: Introduces a lightweight method for assembling video‑centric training data that emphasizes spatial cues, reducing the need for expensive 3D annotations.

Methodology

1. Video Diffusion Backbone – The authors start from a state‑of‑the‑art video diffusion model (e.g., Latent Diffusion for video) that predicts future frames conditioned on a latent representation of prior frames.
2. Scene‑Context Encoder – A transformer‑style encoder ingests a sliding window of past video frames, extracting a spatio‑temporal context vector. No explicit depth or pose is extracted; the encoder learns implicit geometry from motion cues.
3. Instruction Conditioning – Natural‑language commands (e.g., “turn left 30°” or “move to the red chair”) are tokenized and fused with the scene context via cross‑attention.
4. Guided Sampling – During diffusion sampling, a spatial consistency loss penalizes deviations from the inferred 3D layout, encouraging the generated frames to respect the underlying scene geometry.
5. Training Regime – The model is trained on a curated video dataset where each clip is paired with synthetic navigation or grounding instructions. The loss combines standard diffusion reconstruction with the spatial consistency term.

Results & Findings

• Navigation Accuracy – On a held‑out test set, the model correctly follows camera‑pose instructions in ~85% of cases, maintaining realistic perspective and avoiding scene‑breaking artifacts.
• Object Grounding Success – For grounding tasks, the generated video places the camera at the correct target location in 78% of trials, even when the object is partially occluded.
• Long‑Context Stability – Performance degrades gracefully as the context window grows from 4 to 12 seconds, showing the model can retain spatial memory over extended sequences.
• Cross‑Domain Transfer – When evaluated on videos from a completely different domain (e.g., indoor robotics footage vs. synthetic indoor scenes), the model retains >70% success, indicating strong generalization.

Practical Implications

• Robotics & Autonomous Navigation – Video4Spatial could serve as a perception‑only front‑end for robots that need to interpret high‑level commands without costly sensor suites, translating language into feasible motion plans.
• AR/VR Content Generation – Developers can script camera movements or object‑focus cues in natural language, and the system will generate spatially coherent video sequences for immersive experiences.
• Game AI & Cinematics – Game engines could leverage the model to automatically generate cutscenes that respect level geometry, reducing manual camera‑path authoring.
• Video Editing Tools – Editors could ask “zoom to the blue car” or “pan left 45°” and receive a video that respects the scene’s depth, streamlining post‑production workflows.

Limitations & Future Work

• Reliance on Implicit Geometry – Without explicit depth supervision, the model occasionally misestimates scale, especially in highly cluttered scenes.
• Instruction Ambiguity – The system assumes well‑formed, unambiguous commands; handling vague or multi‑step instructions remains an open challenge.
• Computational Cost – Diffusion sampling for high‑resolution video is still expensive, limiting real‑time deployment.
• Future Directions – The authors suggest integrating lightweight depth priors, exploring hierarchical planning for multi‑step tasks, and optimizing sampling (e.g., via distillation) to bring the approach closer to on‑device use.

Authors

• Zeqi Xiao
• Yiwei Zhao
• Lingxiao Li
• Yushi Lan
• Yu Ning
• Rahul Garg
• Roshni Cooper
• Mohammad H. Taghavi
• Xingang Pan

Paper Information

• arXiv ID: 2512.03040v1
• Categories: cs.CV, cs.AI
• Published: December 2, 2025
• PDF: Download PDF