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VideoJAM is our new framework for improved motion generation from AI at Meta We show that video generators struggle with motion because the training objective favors appearance over dynamics. VideoJAM directly adresses this without any extra data or scaling 👇🧵

Hila Chefer

4,609 subscribers

168,926 次观看 • 1 年前 •via X (Twitter)

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Hila Chefer 的头像

Hila Chefer1 年前

Why do video generators struggle with motion? We found that the pixel-based loss barely changes when video frames are shuffled—showing it is nearly **invariant to temporal incoherence**. This leads models to ignore motion and prioritize appearance

Hila Chefer 的头像

Hila Chefer1 年前

Our Solution: VideoJAM VideoJAM instills an explicit motion prior by modifying the objective: the model predicts both appearance and motion from a **single learned representation.** This forces the model to capture both visuals and dynamics, improving motion understanding.

Hila Chefer 的头像

Hila Chefer1 年前

Inner-Guidance: Improving Motion at Inference At inference, we introduce **Inner-Guidance**—a method that leverages the **model’s own motion predictions** as a dynamic guidance signal, steering the generation toward coherent, realistic motion.

Hila Chefer 的头像

Hila Chefer1 年前

🎬 Results VideoJAM fine-tunes a pretrained video generator (DiT) on just 3M samples from its own training set—yet achieves remarkable motion coherence. It even outperforms highly competitive proprietary models like Sora and Kling in motion quality

Hila Chefer 的头像

Hila Chefer1 年前

This work was done during my internship at @AIatMeta 🎉 Huge thanks to my amazing collaborators @urielsinger @amit_zhr @YKirstain @adam_polyak90 Yaniv Taigman @liorwolf and @ShellySheynin Check out the project page for many more results and details:

Hila Chefer 的头像

Hila Chefer1 年前

Now on Huggingface daily papers 🤗 And arxiv 🥳

Akool Inc 的头像

Akool Inc1 年前

Need AI avatars or voiceovers? HeyGen and AKOOL both offer powerful AI video tools, but AKOOL delivers superior results with more precise body movements and lip sync. See the difference!

Alex Nasa 的头像

Alex Nasa1 年前

@AIatMeta A motion prediction auxiliary model make so much sense, did you run it on this benchmark by any chance?

Hila Chefer 的头像

Hila Chefer1 年前

@AIatMeta Interesting, thanks so much for the reference! I actually think physics is an interesting issue that is far (far) from being solved. Our representation is motion-based but I think it’ll be so cool if we could add an actual physics-driven representation to the mix 🙏🙏

Everett World 的头像

Everett World1 年前

@AIatMeta That's super impressive - can't wait to generate with it!

Hila Chefer 的头像

Hila Chefer1 年前

@AIatMeta Thanks Everett! 🫶

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Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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59,835 次观看 • 8 个月前

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This work is not about a new technique. GMT (General Motion Tracking) shows good engineering practices that you can actually train a single unified whole-body control policy for all agile motion, and it works in the real world, directly with sim2real without adaptation. This is different from many existing works that trains/adapts one individual policy for each specific motion. Instead of just showing the beautiful motions/dances, this is actually practical. Because now you can convert all types of commands into real actions in real time without tuning anything. Visit to check out how we handle the following challenges: ✅ Partial Observability. ✅ Hardware Limitations. ✅ Unbalanced Data Distribution. ✅ Model Expressiveness. Work led by Zixuan Chen Mazeyu Ji

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83,347 次观看 • 1 年前

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43,411 次观看 • 3 个月前