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首次观测 · 记录 #1CVF · CVPR 2026:cvf:29d2ddd03c06342269d1448e5019a533CVF · CVPR 2026

TimeRipples: Accelerating vDiTs by Understanding the Spatio-Temporal Correlations in Latent Space

Mao, Wenxuan, Sun, Yulin, Chen, Aiyue, Lin, Jing, Yao, Yiwu, Gan, Yiming, Zhao, Jieru, Leng, Jingwen 等 10 位作者

原文摘要

The recent surge in video generation has shown the growing demand for high-quality video synthesis using large vision models. Existing video generation models are predominantly based on the video diffusion transformer (vDiT), however, they suffer from substantial inference delay due to self-attention. While prior studies have focused on reducing redundant computations in self-attention, they often overlook the inherent spatio-temporal correlations in video streams and directly leverage sparsity patterns from large language models to reduce attention computations. In this work, we take a principled approach to accelerate self-attention in vDiTs by leveraging the spatio-temporal correlations in the latent space. We show that the attention patterns within vDiT are primarily due to the dominant spatial and temporal correlations at the token channel level. Based on this insight, we propose a lightweight and adaptive reuse strategy that approximates attention computations by reusing partial attention scores of spatially or temporally correlated tokens along individual channels. We demonstrate that our method achieves significantly higher computational savings (85%) compared to state-of-the-art techniques over 4 vDiTs, while preserving almost identical video quality (<0.06% loss on VBench).

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:63412320c26605e180e82fa31aa7640aCVF · CVPR 2026

VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking

Lin, Jingyang, Wu, Jialian, Liu, Jiang, Sun, Ximeng, Wang, Ze, Yu, Xiaodong, Luo, Jiebo, Liu, Zicheng 等 9 位作者

原文摘要

Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while maintaining, or even improving, its video understanding capability. VideoSeek operates in a think-act-observe loop with a well-designed toolkit for collecting multi-granular video observations. This design enables query-aware exploration over accumulated observations and supports practical video understanding and reasoning. Experiments on four challenging video understanding and reasoning benchmarks demonstrate that VideoSeek achieves strong accuracy while using far fewer frames than prior video agents and standalone LMMs. Notably, VideoSeek achieves a 10.2 absolute points improvement on LVBench over its base model, GPT-5, while using 93% fewer frames. Further analysis highlights the significance of leveraging video logic flow, strong reasoning capability, and the complementary roles of toolkit design.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8dc707007d5877169df413d9ddef7d59CVF · CVPR 2026

TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

Luo, Guan, Li, Xiu, Chen, Rui, Yi, Xuanyu, Lin, Jing, Chen, Chia Hao, Liu, Jiahang, Zhang, Song-Hai 等 9 位作者

原文摘要

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (e.g., SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an Linfinity distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details, as shown in Fig. 1.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:bd77379340d528127f8ffa6e95a6e743CVF · CVPR 2026

G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

Hu, Wenbo, Lin, Jingli, Long, Yilin, Ran, Yunlong, Jiang, Lihan, Wang, Yifan, Zhu, Chenming, Xu, Runsen 等 10 位作者

原文摘要

Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G^2VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G^2VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations.Experimental results demonstrate G^2VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks.By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G^2VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f70a86b40c9db27d45b0f8e6bdc5462cCVF · CVPR 2026

ViLearn: Accelerating Training Convergence of Image-to-3D Generation via Visibility Learning

Chen, Rui, Zhang, Jianfeng, Lin, Jing, Yi, Xuanyu, Liang, Yixun, Luo, Guan, Li, Xiu, Li, Zeming 等 9 位作者

原文摘要

Single-image-to-3D shape generation has seen remarkable progress, driven by latent diffusion models trained on the compressed latent space of 3D VAEs. However, the task remains intrinsically ill-posed: recovering complete 3D geometry--especially occluded surfaces--from a single view is inherently ambiguous. Existing VecSet-based approaches further exacerbate this challenge by treating shape tokens as an unordered set without explicit positional encoding. This design forces diffusion models to simultaneously learn visible correspondences from the input image and hallucinate invisible geometry within a large, permutation-invariant token space, where the lack of structure significantly hinders training efficiency and convergence stability.To address this, we propose Visibility Learning, a training paradigm that injects visibility structure and positional inductive bias into the image-to-3D pipeline. Our method comprises two synergistic components: (1) Visibility Grouping (VG), which explicitly partitions VecSet tokens into visible and invisible subsets by exploiting the spatial locality of VecSet VAE decoders; and (2) Visibility-Aware Positional Encoding (VAPE), which assigns shared positional embeddings to image tokens and visible shape tokens to amplify their correspondence, while using distinct encodings for invisible tokens to guide hallucination. By explicitly disentangling visible reconstruction from invisible hallucination, our approach shrinks the effective hypothesis space and provides clear structural guidance for diffusion models. Extensive experiments demonstrate that Visibility Learning accelerates training convergence by up to 4.4xwhile achieving superior generation quality compared to strong VecSet-based baselines.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗