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Converting video models ➡️ ego-centric world models capable of humanoids manipulating objects, robots navigating through paintings, and beyond! Huge effort led by Anurag Bagchi over a year! Special thanks to 1X for releasing so much data!

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Homanga Bharadhwaj

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10,478 Aufrufe • vor 6 Monaten •via X (Twitter)

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MrNeRF

27,428 Aufrufe • vor 1 Jahr

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.
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Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Michael Black

22,182 Aufrufe • vor 7 Monaten

Deepest thanks to Dustin Yu & Bianca De Vera for sending us your video message — it was such a special moment to share during the event. You may not have been physically present, but we truly felt your love for all the Cheffies here in the UAE. 💛 To our head admin, Rilag 💫 — thank you so much for helping us get those video messages! Even though you were busy that day, you still made time for us, and we’re so grateful. ☺️ A huge shoutout to our biggest sponsor, Ate quidditchgaL🐰🇵🇭🇦🇪 — you know how much joy and gratitude I feel every time you send those unexpected freebies (Tote bags, Valaya perfumes and more)! You always go above and beyond🫶🏻 And to my Witches OGs GDM — thank you for always supporting my plans, listening to my rants, and walking with me everywhere to find this and that. Your effort and love mean the world! 🥹 Lastly, to everyone who attended and helped make this event a success, thank you from the bottom of my heart! 🌸 -Admin Choco
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Deepest thanks to Dustin Yu & Bianca De Vera for sending us your video message — it was such a special moment to share during the event. You may not have been physically present, but we truly felt your love for all the Cheffies here in the UAE. 💛 To our head admin, Rilag 💫 — thank you so much for helping us get those video messages! Even though you were busy that day, you still made time for us, and we’re so grateful. ☺️ A huge shoutout to our biggest sponsor, Ate quidditchgaL🐰🇵🇭🇦🇪 — you know how much joy and gratitude I feel every time you send those unexpected freebies (Tote bags, Valaya perfumes and more)! You always go above and beyond🫶🏻 And to my Witches OGs GDM — thank you for always supporting my plans, listening to my rants, and walking with me everywhere to find this and that. Your effort and love mean the world! 🥹 Lastly, to everyone who attended and helped make this event a success, thank you from the bottom of my heart! 🌸 -Admin Choco

WITCHES OF DUSTBIA INTERNATIONAL🧙‍♂️

30,120 Aufrufe • vor 9 Monaten

2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!
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2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!

@levelsio

584,228 Aufrufe • vor 10 Monaten

30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:
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30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

17,077 Aufrufe • vor 2 Monaten

Look, Kaimates! Kaisha mentioned us the “Team Kaimates Official” and we truly felt all the love and appreciation! A huge thank you to our amazing team for all the hard work and dedication that keeps this community thriving. We’re also incredibly grateful to our Team Live, who put so much effort into making this happen—just to show and feel how deeply loved Kaisha truly is. Your passion and dedication mean so much to all of us. And thank you, Kaisha, for always being so accommodating and kind to your fans even when you’re sick and not feeling your best, you still push through to make everyone feel special. That kind of heart and selflessness is truly rare. You deserve the world! DUBAI MEETS KAI #KaiMontinola | kai ☆
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Look, Kaimates! Kaisha mentioned us the “Team Kaimates Official” and we truly felt all the love and appreciation! A huge thank you to our amazing team for all the hard work and dedication that keeps this community thriving. We’re also incredibly grateful to our Team Live, who put so much effort into making this happen—just to show and feel how deeply loved Kaisha truly is. Your passion and dedication mean so much to all of us. And thank you, Kaisha, for always being so accommodating and kind to your fans even when you’re sick and not feeling your best, you still push through to make everyone feel special. That kind of heart and selflessness is truly rare. You deserve the world! DUBAI MEETS KAI #KaiMontinola | kai ☆

TEAM KAIMATES OFFICIAL

12,754 Aufrufe • vor 6 Monaten

A 27-year-old guy from China made $150,000 using AI simply by automating content creation for models It all started with a common issue agencies face: models can't shoot enough content to scale. The strategy turned out to be ridiculously simple: 1) Upload a single photo of a girl. 2) Pick a motion or dance template from the library. 3) The AI generates a realistic video for Reels, TikTok, and Shorts in seconds. By leveraging this setup, he automated mass posting, publishing hundreds of videos daily without needing real models. The system pulls in millions of free organic views, driving traffic directly through profile links to paid subscriptions, OnlyFans, and affiliate offers. AI tools are completely solving the content shortage problem, allowing solopreneurs to run multi-million-dollar virtual agencies on their own. Bookmark this
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A 27-year-old guy from China made $150,000 using AI simply by automating content creation for models It all started with a common issue agencies face: models can't shoot enough content to scale. The strategy turned out to be ridiculously simple: 1) Upload a single photo of a girl. 2) Pick a motion or dance template from the library. 3) The AI generates a realistic video for Reels, TikTok, and Shorts in seconds. By leveraging this setup, he automated mass posting, publishing hundreds of videos daily without needing real models. The system pulls in millions of free organic views, driving traffic directly through profile links to paid subscriptions, OnlyFans, and affiliate offers. AI tools are completely solving the content shortage problem, allowing solopreneurs to run multi-million-dollar virtual agencies on their own. Bookmark this

Ridark

40,991 Aufrufe • vor 15 Tagen

World modeling and imitation learning have largely been considered two disparate worlds. In our recent work, Unified World Models, just accepted to #RSS2025, Chuning Zhu provides a dead-simple unifying solution: just train a joint diffusion model over actions and future states, but with *decoupled* diffusion time steps across these modalities. Manipulating these decoupled time steps then allows for marginalization or conditioning on actions or states; a single model can serve as a policy, forward dynamics model, video prediction model, or inverse dynamics model by simply setting diffusion timesteps carefully. The resulting model can leverage video datasets along with robot training data much more effectively, and shows improved robustness, generalization, and flexibility. This is exciting because it is frustratingly simple, scalable, and shows strong improvement on real-world robotics problems. Please refer to Chuning Zhu 's excellent thread for more details! More details/code can be found on our website and in the paper -
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World modeling and imitation learning have largely been considered two disparate worlds. In our recent work, Unified World Models, just accepted to #RSS2025, Chuning Zhu provides a dead-simple unifying solution: just train a joint diffusion model over actions and future states, but with *decoupled* diffusion time steps across these modalities. Manipulating these decoupled time steps then allows for marginalization or conditioning on actions or states; a single model can serve as a policy, forward dynamics model, video prediction model, or inverse dynamics model by simply setting diffusion timesteps carefully. The resulting model can leverage video datasets along with robot training data much more effectively, and shows improved robustness, generalization, and flexibility. This is exciting because it is frustratingly simple, scalable, and shows strong improvement on real-world robotics problems. Please refer to Chuning Zhu 's excellent thread for more details! More details/code can be found on our website and in the paper -

Abhishek Gupta

11,430 Aufrufe • vor 1 Jahr