[Paper] RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation

Source: arXiv - 2601.05241v1

Overview

The paper RoboVIP tackles a bottleneck in robot learning: the scarcity of diverse, high‑quality manipulation data. By marrying diffusion‑based video generation with visual identity prompting—using exemplar images as guidance—the authors can synthesize multi‑view, temporally coherent videos that look like real robot episodes. This synthetic data can be plugged into modern vision‑language‑action (VLA) and visuomotor policies, delivering measurable performance lifts both in simulation and on real hardware.

Key Contributions

• Visual Identity Prompting (VIP): Introduces exemplar‑image conditioning for diffusion models, enabling precise control over scene layout, object appearance, and camera viewpoints.
• Multi‑View Video Generation Pipeline: Extends text‑to‑image diffusion to generate synchronized videos from several camera angles, preserving temporal coherence across frames.
• Scalable Identity Pool Construction: Presents an automated method to harvest visual identity exemplars from existing large‑scale robotics datasets (e.g., RoboSuite, RLBench).
• Empirical Validation Across Domains: Demonstrates consistent gains when training VLA and end‑to‑end visuomotor policies on synthetic data, in both simulated environments and on a real‑world robot arm.
• Open‑Source Toolkit: Releases code, pretrained diffusion checkpoints, and the curated identity pool to foster reproducibility and community extensions.

Methodology

1. Data Curation:

   • Crawl thousands of manipulation episodes from public robotics datasets.
   • Extract visual identities—distinct objects, backgrounds, and robot configurations—by clustering image embeddings and selecting representative frames.

2. Diffusion Model Conditioning:

   • Base model: a state‑of‑the‑art video diffusion architecture (e.g., Stable Diffusion‑Video).
   • Conditioning inputs: (a) a textual description of the task (e.g., “pick the red block”), and (b) one or more exemplar images that encode the exact object shape, texture, and camera pose.
   • The model learns to fuse textual semantics with visual cues, producing videos that respect both constraints.

3. Multi‑View Synthesis:

   • Generate a primary view video, then feed intermediate latent representations to sibling diffusion branches that render the same scene from additional calibrated camera poses.
   • A temporal consistency loss aligns motion across views, ensuring that the robot’s arm trajectory is coherent in all streams.

4. Policy Training:

   • Augment the original dataset with the synthetic multi‑view videos.
   • Train downstream policies (e.g., CLIP‑based VLA models, transformer‑based visuomotor networks) using standard RL or imitation‑learning pipelines.

Results & Findings

• Temporal Coherence: Human evaluators rated RoboVIP videos as “smooth” in 93 % of cases vs. 68 % for prior text‑only diffusion methods.
• Identity Fidelity: The generated objects matched the exemplar appearance within a mean L2 distance of 0.12 in latent space, far better than text‑only baselines (0.34).
• Training Efficiency: Adding synthetic data reduced the number of real episodes needed to reach a target performance by ~30 %.

Practical Implications

• Rapid Data Expansion: Teams can multiply their existing manipulation logs by orders of magnitude without additional hardware, accelerating the data‑hungry pre‑training phase of robot policies.
• Domain Transfer: By swapping exemplar images, the same diffusion model can generate scenes for new workspaces (different table textures, lighting, or object sets) without retraining.
• Multi‑Camera Systems: RoboVIP’s synchronized multi‑view output fits naturally into modern robot setups that rely on several RGB cameras for depth‑free perception, simplifying data collection pipelines.
• Safety & Cost Savings: Synthetic episodes can explore risky or failure‑prone configurations (e.g., near‑collision trajectories) safely, enriching the policy’s robustness before deployment on real hardware.

Limitations & Future Work

• Simulation‑Reality Gap: Although performance improves, synthetic videos still lack the fine‑grained physics cues (e.g., subtle object deformation) present in real footage, limiting gains for highly dynamic tasks.
• Scalability of Identity Pool: The current clustering approach may miss rare objects; future work could incorporate active learning to query humans for missing identities.
• Real‑Time Generation: Generation currently runs offline; integrating a lightweight, on‑the‑fly diffusion model could enable just‑in‑time data augmentation during policy training.
• Broader Modalities: Extending VIP to incorporate depth maps, tactile signals, or proprioceptive embeddings would make the synthetic data even richer for multimodal policies.

Authors

• Boyang Wang
• Haoran Zhang
• Shujie Zhang
• Jinkun Hao
• Mingda Jia
• Qi Lv
• Yucheng Mao
• Zhaoyang Lyu
• Jia Zeng
• Xudong Xu
• Jiangmiao Pang

Paper Information

• arXiv ID: 2601.05241v1
• Categories: cs.CV, cs.AI, cs.RO
• Published: January 8, 2026
• PDF: Download PDF