正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

FlowMo Variance-Based Flow Guidance for Coherent Motion in Video Generation

AK

515,030 subscribers

16,471 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

2 条评论

AK 的头像

AK1 年前

discuss with author:

Solar Heavy 的头像

Solar Heavy2 年前

Come Around Music Video out now

相关视频

Wan-Move Motion-controllable Video Generation via Latent Trajectory Guidance

Wan-Move Motion-controllable Video Generation via Latent Trajectory Guidance

AK

14,872 次观看 • 8 个月前

Alibaba just announced FantasyTalking on Hugging Face Realistic Talking Portrait Generation via Coherent Motion Synthesis

Alibaba just announced FantasyTalking on Hugging Face Realistic Talking Portrait Generation via Coherent Motion Synthesis

AK

69,968 次观看 • 1 年前

Motion Attribution for Video Generation

Motion Attribution for Video Generation

AK

10,904 次观看 • 7 个月前

New #NVIDIA Paper We introduce Motive, a motion-centric, gradient-based data attribution method that traces which training videos help or hurt video generation. By isolating temporal dynamics from static appearance, Motive identifies which training videos shape motion in video generation. 🔗 1/10

New #NVIDIA Paper We introduce Motive, a motion-centric, gradient-based data attribution method that traces which training videos help or hurt video generation. By isolating temporal dynamics from static appearance, Motive identifies which training videos shape motion in video generation. 🔗 1/10

Xindi Wu

142,121 次观看 • 7 个月前

MotionStreamer announced on Hugging Face Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space

MotionStreamer announced on Hugging Face Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space

AK

43,422 次观看 • 1 年前

Meta presents OneStory Coherent Multi-Shot Video Generation with Adaptive Memory

Meta presents OneStory Coherent Multi-Shot Video Generation with Adaptive Memory

AK

11,532 次观看 • 8 个月前

InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

AK

71,232 次观看 • 1 年前

VideoComposer brings ControlNet guidance to text and video-to-video. The model enables to combine multiple modalities like text, sketch, style and even motion to drive video generation. The results look extremely good.

VideoComposer brings ControlNet guidance to text and video-to-video. The model enables to combine multiple modalities like text, sketch, style and even motion to drive video generation. The results look extremely good.

Dreaming Tulpa 🥓👑

18,045 次观看 • 3 年前

Now my SVG based 3D graphics app “dim” has motion path as well as spline flow.

Now my SVG based 3D graphics app “dim” has motion path as well as spline flow.

ishikawa masaya / 石川将也

28,786 次观看 • 1 个月前

We present Neural Riemannian Motion Fields (NRMF) as a promising paradigm for generative modeling of articulated motion. Unlike various diffusion based models, NRMF is useful both in generation and in deployment as a prior across tasks: #CVPR #AI #CVML👇

We present Neural Riemannian Motion Fields (NRMF) as a promising paradigm for generative modeling of articulated motion. Unlike various diffusion based models, NRMF is useful both in generation and in deployment as a prior across tasks: #CVPR #AI #CVML👇

Tolga Birdal

16,795 次观看 • 11 个月前

Introducing Grok Imagine v0.9, our new video generation model with massive upgrades from v0.1 in visual quality, motion, audio generation, and more. Try it for free in the Grok App.

Introducing Grok Imagine v0.9, our new video generation model with massive upgrades from v0.1 in visual quality, motion, audio generation, and more. Try it for free in the Grok App.

Grok

214,749,903 次观看 • 10 个月前

Meet Gemini Omni Flash on SeaArt. A natively multimodal model for video generation and conversational editing. Edit with natural language. Keep scenes and story consistent across edits. Motion follows real-world physics and logic. From prompt to scene — in one flow.

Meet Gemini Omni Flash on SeaArt. A natively multimodal model for video generation and conversational editing. Edit with natural language. Keep scenes and story consistent across edits. Motion follows real-world physics and logic. From prompt to scene — in one flow.

SeaArt.Ai🐋

30,455 次观看 • 1 个月前

Introducing Imagine v0.9, our new video generation model with massive upgrades from v0.1 in visual quality, motion, audio generation, and more. Now available for free on all our products:

Introducing Imagine v0.9, our new video generation model with massive upgrades from v0.1 in visual quality, motion, audio generation, and more. Now available for free on all our products:

SpaceXAI

8,314,948 次观看 • 10 个月前

New Kling, Runway, Luma competitor? Open text and image video generation model Pyramidal Flow Matching for Efficient Video Generative Modeling

New Kling, Runway, Luma competitor? Open text and image video generation model Pyramidal Flow Matching for Efficient Video Generative Modeling

AK

91,250 次观看 • 1 年前

ByteDance just dropped Goku Flow Based Video Generative Foundation Models

ByteDance just dropped Goku Flow Based Video Generative Foundation Models

AK

248,359 次观看 • 1 年前

RobotMDM, by Disney Research, combines diffusion-based motion generation with RL to produce physics-aware humanoid motions from text prompts. Trained on human motion data with a reward surrogate for physical feasibility, it ensures realistic motions.

RobotMDM, by Disney Research, combines diffusion-based motion generation with RL to produce physics-aware humanoid motions from text prompts. Trained on human motion data with a reward surrogate for physical feasibility, it ensures realistic motions.

The Humanoid Hub

22,943 次观看 • 1 年前

Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

AK

102,706 次观看 • 2 年前

Got five papers accepted by #ECCV2024 European Conference on Computer Vision #ECCV2026 ! Huge thanks to all my collaborators! 😃 See you in Milan 🇮🇹 Summary of Selected Works (I made a fast-forward for them 😄) - [Shape Generation] Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models, ECCV 2024. - [Efficient Motion Generation] EMDM: Efficient Motion Diffusion Model for Fast, High-Quality Human Motion Generation, ECCV 2024. - [Controllable Motion Generation] TLControl: Trajectory and Language Control for Human Motion Synthesis, ECCV 2024. - [Avatar Generation] Disentangled Clothed Avatar Generation from Text Descriptions, ECCV 2024. Project Page: Surf-D: EMDM: TLControl: SOSMPL:

Zhiyang (Frank) Dou

18,192 次观看 • 2 年前

Dream2Flow Bridging Video Generation and Open-World Manipulation with 3D Object Flow

Dream2Flow Bridging Video Generation and Open-World Manipulation with 3D Object Flow

AK

29,980 次观看 • 7 个月前

Your content creation flow just got faster. Veo3 Fast is now live in Hedra! Generate high-motion video fast for less credits than standard Veo3. See the speed for yourself in Hedra Studio.

Your content creation flow just got faster. Veo3 Fast is now live in Hedra! Generate high-motion video fast for less credits than standard Veo3. See the speed for yourself in Hedra Studio.

Hedra

2,247,258 次观看 • 1 年前