Open-Source RL for 3D Scene Editing
Alibaba researchers released RL3DEdit, a reinforcement learning framework for 3D scene editing that's twice as fast as traditional methods 1.
RL3DEdit uses reinforcement learning (RL) to ensure that edits made to 3D scenes are consistent across multiple views. It addresses the challenge of maintaining consistency when using 2D diffusion models for 3D editing. The framework leverages a 3D foundation model called VGGT to provide geometric reward signals based on depth and point cloud confidence. These rewards guide the RL process, helping to anchor 2D editing priors onto a 3D-consistent manifold. This approach bypasses the need for large amounts of 3D-consistent training data. Alibaba will release the code and model for RL3DEdit to promote further development in 3D editing. The framework achieves high-quality results across diverse editing scenarios, including motion edits, subject replacement, style transfer, background changes, and scene addition. It also delivers a speedup of over 2x compared to existing methods. RL3DEdit uses the FLUX-Kontext model as a backbone, enabling global cross-view attention for joint image editing. By using RL for verification, the framework offers a more efficient alternative to supervised training for complex 3D tasks.
