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新论文 · v1arXiv:2611.17298

基于显式对应关系的一致视频到视频翻译

Consistent Video-to-Video Translation via Explicit Correspondences

Gaurav Parmar, Zhengqi Li, Richard Zhang, Jun-Yan Zhu, Srinivasa Narasimhan, Eli Shechtman, Yotam Nitzan

AI 速读 基于摘要

作者提出 vid2vid-long，利用输入视频中的 patch 级对应关系检索并复用历史生成结果，以在保持实时性的同时提升长程时序一致性。

cs.CV · cs.AI · cs.GR · cs.LG

arXiv ↗PDF ↗
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Interactive video-to-video applications require real-time generation while maintaining long-range temporal consistency. However, recent methods achieve speed by restricting attention to a fixed-length causal window. This limits long-range consistency whenever relevant history falls outside the context window. To address this limitation, we introduce vid2vid-long, a correspondence-based approach grounded in a simple observation. Video-to-video translation provides an explicit cross-frame reference: the regions that match in the input video should match in the output video too. Concretely, we compute patch-level correspondences in the input, guiding retrieval of previously generated outputs in the corresponding regions, and condition the model on these patches through correspondence cross-attention. Our experiments demonstrate that vid2vid-long consistently improves long-range temporal consistency across recent video models while preserving generation quality and real-time performance.

版本更新 · v4arXiv:2606.13898

HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing Model Serving

Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou 等 11 位作者

原文摘要

Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However, current generative AI models face significant latency challenges, which become even more pronounced as the field transitions from convolution-based U-Nets to Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing samples spanning a wide range of mask ratios, the DiT module alone accounts for an average of 73% of the total model latency, even after being distilled from 50 timesteps down to 8 timesteps. To tackle this challenge, we propose $\textbf{HiLo-Token}$, an input-adaptive token compression framework that allocates a larger token budget to high-frequency, rich-context regions while assigning fewer tokens to low-frequency areas. Specifically, for the editing region specified by the user mask, we retain all tokens within a dilated mask to preserve strong locality and contextual relevance. Outside the editing region, we introduce a simple yet effective high-frequency token selection strategy based on spatial frequency to capture important local details, while using tokens from a heavily downsampled image to represent low-frequency components and preserve the coarse global structure. Extensive experiments on production-level evaluation data validate the effectiveness of the proposed method, which achieves 3.13$\times$, 2.59$\times$, and 1.67$\times$ DiT speedups on A100-80GB for image editing tasks across small, medium, and large mask ratio categories with average ratios of 6.38%, 15.92%, and 35.36%, respectively, without any regression in generation quality. In production, where the editing pipeline is served as disaggregated, independently autoscaled GPU workers, these per-request savings translate into a 33% reduction in the GPUs required to serve the models.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07055

Expanding the Quality-Coverage Frontier with Competitive Flow Matching

Wenxuan Peng, Richard Zhang, Haitian Zheng, Jaskirat Singh, Tsai-Shien Chen, Eli Shechtman, Bharath Hariharan, Hadar Averbuch-Elor 等 9 位作者

原文摘要

Flow matching (FM) generators often face a quality-coverage tradeoff. Inference-time techniques such as classifier-free guidance raise quality, but at the cost of coverage. We hypothesize this limitation stems from a basic property of the objective: an intermediate noisy image could correspond to many clean images, yet the model can only predict a single velocity, at best averaging over the multiple possibilities. To mitigate this, we introduce competition among predictions during training. We achieve this by augmenting the predictor with a randomly drawn low-dimensional conditioning code $z$. For each training example, we sample $K$ values of $z$ to produce $K$ candidate velocities that compete to explain the target, and apply the loss only to the best-matching candidate. This winner-take-all objective allows different modes to shape different predictions, rather than being averaged into a single compromise. Furthermore, this term naturally enables a new form of guidance, auxiliary conditioning scaling, which complements traditional guidance methods. Together, these two guidance mechanisms show that our method substantially improves the quality-coverage frontier. We validate on ImageNet-256, using two different testbeds, SiT and RAEv2. Given less than 3% of the original SiT pretraining compute for post-training, at the 67% Recall point, our method reduces $\mathrm{FD}_{r,6}$ from 19.22 to 5.91. On RAEv2, at the 70% Recall point, it reduces $\mathrm{FD}_{r,6}$ from 16.83 to 3.18.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38467

StreamStudio: Exploring Video Authoring with Streaming Autoregressive Generation

Boyu Li, Zhe Lin, Eli Shechtman, Ding Li, Hongbo Fu, Yuqian Zhou

原文摘要

We present StreamStudio, a prototype system that explores a new authoring paradigm for streaming video generation. Instead of entering a prompt and waiting for a complete video before making changes, creators can interact with the generation as it unfolds: steering what comes next, returning to earlier moments, and exploring different continuations. In this way, StreamStudio shifts creative control from post-generation revision to continuous interaction with the generative process. Our contribution centers on this authoring paradigm and the interaction mechanisms that make streaming generation directly manipulable. StreamStudio is designed as a plug-and-play interface that can work with compatible generation and editing models, allowing its interaction model to evolve alongside advances in underlying video models. As streaming models become faster and easier to control in the future, such an interface may support more flexible and controllable video creation, including richer edits and multimodal interventions during generation.

cs.HC

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arXiv ↗PDF ↗