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🕹️We are excited to introduce "ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation" ChronoEdit reframes image editing as a video generation task to encourage temporal consistency. It leverages a temporal reasoning stage that denoises with “video reasoning tokens” to "reason" on physically plausible edits. See the attached... show more

Huan Ling

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36,895 görüntüleme • 10 ay önce •via X (Twitter)

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🔥Ultra-Long Video World Model up to 5min🔥 ✨ We introduce #LongVie2, an end-to-end autoregressive video world model that supports continuous video generation lasting up to 5min with: 🕹️ Strong Controllability 📷 Long-term Visual Fidelity 🔒 Temporal Consistency - Project: - Code: - Paper: . Thanks to AK !

🔥Ultra-Long Video World Model up to 5min🔥 ✨ We introduce #LongVie2, an end-to-end autoregressive video world model that supports continuous video generation lasting up to 5min with: 🕹️ Strong Controllability 📷 Long-term Visual Fidelity 🔒 Temporal Consistency - Project: - Code: - Paper: . Thanks to AK !

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AK

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New Qwen 2511 features: - Stronger Multi‑Person Consistency - Built‑in Community LoRAs - Reduced Image drift with Better identity stability - Improved Geometric Reasoning for Structural edits Try today and take image editing to the next level. Follow us for more updates!

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Alibaba Cloud

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Zhiwen(Aaron) Fan

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AK

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AK

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Infinite-Realities

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AK

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Current world models often separate spatial reasoning from temporal modeling, leading to consistency bottlenecks and scaling limits. We developed RAYNOVA, a 4D world foundation model, to address this by unifying space and time into a single representation via a pure auto-regressive framework. The result: Data-driven high-fidelity simulation data that scales across any vehicles, anywhere. Read the full technical breakdown:

Applied Intuition

11,252 görüntüleme • 2 ay önce

1/ Excited to announce MoCA - a simple yet effective video editing method that accomplishes diverse spatial (style, background, object) and motion video edits using a motion conditioned image animation model.

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Wilson Yan

10,647 görüntüleme • 2 yıl önce

🚀 Q2 Image Model is Live! ✨Text to image, Reference to image, and Image editing supported ✨Ultra-fast generation (as fast as 5s), 4K quality, super consistency ✨One-stop workflow: turn reference images into subjects and reuse them for video creation ✨Unlimited image generation for members until Dec 31 New users: use code VIDUQ2RTI for bonus credits 🎁 #ViduQ2RTI #ViduQ2 #Viduai #vidu

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Vidu AI

24,747 görüntüleme • 8 ay önce

TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.

TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.

AK

143,469 görüntüleme • 3 yıl önce

We have released an update for both text to video and image to video generation with Gen-2, bringing major improvements to both the fidelity and consistency of video results. Try it now at

We have released an update for both text to video and image to video generation with Gen-2, bringing major improvements to both the fidelity and consistency of video results. Try it now at

Runway

601,457 görüntüleme • 2 yıl önce

Ok finally dug into Meta's new Movie Gen paper. Text-to-video is cool and all but, to me the precise editing feature is the game changer. I mean just look at these results 🤯 It can handle complex VFX tasks like replacing environments, doing set extensions, swapping characters, removing items, adding particle effects with realistic lighting interaction. The coolest bit to me is how they trained this model, because paired before/after vfx editing datasets are super scarce. TL;DR They taught it video editing through a clever three-stage process: 1. Started with image editing data, treating it like single-frame video edits. 2. Created synthetic video editing tasks by animating still image edits and using AI models (like SAM and DINO) for object segmentation. 3. The model generated edited videos, and then learned to reconstruct the originals from the edited version Meta calls this "video editing via backtranslation" and the results speak for themselves.

Ok finally dug into Meta's new Movie Gen paper. Text-to-video is cool and all but, to me the precise editing feature is the game changer. I mean just look at these results 🤯 It can handle complex VFX tasks like replacing environments, doing set extensions, swapping characters, removing items, adding particle effects with realistic lighting interaction. The coolest bit to me is how they trained this model, because paired before/after vfx editing datasets are super scarce. TL;DR They taught it video editing through a clever three-stage process: 1. Started with image editing data, treating it like single-frame video edits. 2. Created synthetic video editing tasks by animating still image edits and using AI models (like SAM and DINO) for object segmentation. 3. The model generated edited videos, and then learned to reconstruct the originals from the edited version Meta calls this "video editing via backtranslation" and the results speak for themselves.

Bilawal Sidhu

50,775 görüntüleme • 1 yıl önce

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing abs: paper page: present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis.Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline.We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video.With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field.We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training.More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing abs: paper page: present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis.Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline.We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video.With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field.We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training.More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.

AK

153,241 görüntüleme • 3 yıl önce

[SIGGRAPH '26] Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation TL;DR: We present ATGS, a novel framework for volumetric video reconstruction that effectively handles long sequences and complex motions. By utilizing time-conditioned anchors and a temporal windowing strategy, ATGS enhances temporal coherence and scalability. Abstract (excerpt): Key insight is that explicitly tracking long term complex motion with individual Gaussian primitives is inherently unstable. Instead, we organize Gaussians around time conditioned anchors that localize their spatial and temporal support, thereby reducing long range motion complexity. We further introduce a temporal windowing strategy to activate only anchors relevant to the queried time, which improves scalability and temporal coherence. In addition, to ensure spatial and temporal stability, we design a compact set of multi level anchor features that encode global features, local spatial features, and local temporal features, jointly constraining Gaussian generation. Extensive experiments demonstrate that ATGS consistently outperforms prior methods on long sequence volumetric videos with complex motions.

[SIGGRAPH '26] Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation TL;DR: We present ATGS, a novel framework for volumetric video reconstruction that effectively handles long sequences and complex motions. By utilizing time-conditioned anchors and a temporal windowing strategy, ATGS enhances temporal coherence and scalability. Abstract (excerpt): Key insight is that explicitly tracking long term complex motion with individual Gaussian primitives is inherently unstable. Instead, we organize Gaussians around time conditioned anchors that localize their spatial and temporal support, thereby reducing long range motion complexity. We further introduce a temporal windowing strategy to activate only anchors relevant to the queried time, which improves scalability and temporal coherence. In addition, to ensure spatial and temporal stability, we design a compact set of multi level anchor features that encode global features, local spatial features, and local temporal features, jointly constraining Gaussian generation. Extensive experiments demonstrate that ATGS consistently outperforms prior methods on long sequence volumetric videos with complex motions.

MrNeRF

27,839 görüntüleme • 4 ay önce