Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation

We introduce Edit4D, an editable text-to-4D scene generation framework that constructs 4D scenes from separate background and dynamic character representations. Recent advances in generative models have enabled text-driven dynamic scene synthesis, but editable 4D scene generation remains challenging due to the need to preserve spatial geometry and temporal dynamics. Existing approaches typically generate holistic scene videos, where the background and dynamic characters are coupled, making object-level editing and flexible camera control difficult. Edit4D addresses these limitations through a stage-specific generation pipeline. Given a user instruction, Edit4D first decomposes it into stage-specific prompts for background, dynamic character, and scene-character composition. Using these prompts, we synthesize multi-view scene images with text-to-image and camera-conditioned generative priors and reconstruct a background 3D Gaussian splatting representation. We then generate a character image, animate it with an image-conditioned video prior, reconstruct a 4D mesh, and bind Gaussian primitives to obtain a character representation. Finally, both components are composed through text-guided segmentation and contact-aware optimization. A global scene refinement is then performed to improve harmonization and rendering consistency. By integrating stage-specific components, Edit4D enables controllable camera motion, flexible object-level editing, and the practical construction of editable 4D scenes. Project page: https://chanwo0kim.github.io/Edit4D/

Generative models have rapidly pushed content creation be-yond 2D imagery toward dynamic 3D and 4D scene synthesis. Yet pro-ducing realistic and temporally stable 4D content from a single image is still difficult because one view provides limited structural cues and weak motion evidence. We introduce MATE4D, a framework that converts one input image into editable dynamic 4D content. Our method constructs a spatio-temporal multi-view image matrix with text-guided background manipulation, delivering coherent supervision over viewpoint, appear-ance, and motion. These synthesized observations are used to optimize 3D Gaussian primitives, which are then animated through a lightweight deformation module to form a 4D representation. The resulting scenes preserve geometry more faithfully, maintain smoother temporal behavior, and keep background edits more consistent, reducing context ambiguity and motion artifacts. Experiments on Objaverse-XL and Diffusion4D show that MATE4D outperforms strong baselines in visual quality, effi-ciency, and controllability, supporting practical AR/VR content creation.

Recent advancements in text-guided 3D editing, particularly with 3D Gaussian Splatting (3DGS), have shown promise but still lag behind the flexibility of 2D editing tools. Existing methods face a critical trade-off between preserving multi-view consistency and allowing structural modifications. Image-based editors can also struggle to apply the intended edit to objects viewed from unusual angles. To address these limitations, we introduce VidEdit3D, a framework that reformulates 3D scene editing as a trajectory-based video editing task. By drawing a parallel between camera movement in static 3D scenes and temporal dynamics in video, we leverage the robust spatiotemporal priors of pre-trained Video Diffusion Models (VDMs) to encourage semantic and structural coherence across views. Specifically, we propose Trajectory-Guided Rendering (TGR) to select informative keyframes and construct smooth video clips aligned with VDM input requirements. To propagate edits from an anchor view to these clips, we develop Flow-Aligned Video Editing (FAVE), a training-free framework incorporating an Editing Consistency Guidance (ECG) mechanism. ECG contrasts source and edited image conditions at a shared latent state, using scheduled source-image amplification to strengthen adherence to the edited reference while maintaining original scene context. Finally, we introduce Region-Selective 3DGS Refinement (RS-GS) to strictly confine geometric and appearance updates to target primitives, reducing background attribute leakage. Experiments demonstrate stronger instruction alignment and better multi-view consistency than the baselines.