arXiv subjects

Ming-Hsuan Yang

Publications and source records attributed to Ming-Hsuan Yang.

3 recordsLinked to original sources

arXiv2026

ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare

Online novel view synthesis requires a model to reconstruct a scene causally from a stream of observations while keeping it renderable at every moment. We present ReCoSplat, an online feed-forward Gaussian Splatting model supporting both posed and unposed inputs, with or without camera intrinsics. While assembling local Gaussians with camera poses scales better than canonical-space prediction, stable training requires ground-truth poses, creating a distribution mismatch when predicted poses are used at inference. To address this, we introduce a Render-and-Compare (ReCo) module. ReCo renders the accumulated scene from the viewpoint of the incoming observation, comparing the render with the observation to produce a stable conditioning signal that helps bridge the mismatch. To support long sequences, we propose a hybrid KV-cache compression strategy combining early-layer truncation with chunk-level selective retention, reducing the KV cache size by over 90% for 100 or more frames. ReCoSplat achieves state-of-the-art performance among online methods while processing 256-view streams at an average input throughput of 45.1 FPS, with an end-of-stream throughput of 41.1 FPS on an RTX 6000 Ada GPU. Code and pretrained models are released at https://freemancheng.com/ReCoSplat .

cs.CV↗

arXiv2026

Chat-Edit-3D++: Interactive 3D and 4D Scene Editing via Large Language Models

Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still have certain shortcomings, hindering their further development as interactive design tools. Such schemes typically adhere to fixed input patterns, limiting flexibility in text input. Furthermore, their editing capabilities are constrained by a single or a few 2D visual models and require intricate pipeline design to integrate these models into 3D reconstruction processes. To address the aforementioned issues, we propose the Hash-Atlas network, which reformulates 3D scene editing as operations on 2D atlas images, thereby achieving a workflow decoupling of the 2D editing and 3D reconstruction processes. Building on this foundation, we introduce a dialogue-based 3D scene editing approach, termed CE3D++, which is centered on a large language model (LLM) that allows arbitrary textual input from users and interprets their intentions, subsequently facilitating the autonomous invocation of the corresponding visual models. Additionally, we extend CE3D++ to monocular 4D scenes by imposing motion constraints on moving objects and further fine-tuning the LLM by creating a trajectory dataset related to editing tasks, which enables the smaller LLM to schedule up to 30 different visual tools accurately. Experimental results demonstrate that CE3D++ effectively integrates multiple visual models to achieve diverse visual editing effects, possessing strong scene comprehension and multi-round dialog capabilities. The source codes and trained models are available at https://github.com/Fangkang515/CE3D.

cs.CV↗

arXiv2026

Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding

Spatio-temporal video grounding (STVG) requires models to identify when a referred event occurs and localize the target entity throughout that interval. Existing multimodal large language models typically serialize dense localization trajectories autoregressively, causing decoding latency to grow with tube length and allowing localization errors to propagate across time. We introduce Parallel Tube Decoding (PTD), a generative formulation that decomposes grounding into a temporal block followed by time-conditioned spatial blocks decoded simultaneously. This removes both token-level and trajectory-level dependencies, reducing the sequential decoding depth to a fixed $1 + 1$ rounds, independent of tube length. To enable parallel spatial generation, we introduce Decoupled Block Attention, which preserves access to shared video-query context while eliminating cross-box dependencies, together with localization-aware policy optimization for temporal boundaries and spatial geometry. On VidSTG, PTD reduces Tube Completion Latency by 79x and increases spatial decoding throughput by 92x over standard autoregressive decoding, while also improving grounding accuracy. With a compact 4B backbone, our model performs favorably well on VidSTG and HC-STVG, and generalizes zero-shot to temporal grounding, grounded VideoQA, and referring video object tracking. Our results show parallel tube generation is an efficient and effective alternative to autoregressive localization in videos.

cs.CV↗