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Check out Christian Diller's CG-HOI :) We generate realistic 3D human-object interactions, from object geometry and text description. A key ingredient is explicit modeling of contact, during training and as guidance during inference.

Angela Dai

9,758 subscribers

20,514 görüntüleme • 2 yıl önce •via X (Twitter)

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Michael Black profil fotoğrafı

Michael Black2 yıl önce

@chrdiller Wow. Nice work!

Behzad Tabari profil fotoğrafı

Behzad Tabari2 yıl önce

@chrdiller Revolutionizing the animation industry and Hollywood 👍☄️

D. hans profil fotoğrafı

D. hans2 yıl önce

@chrdiller hello, would you be willing to share the code for this paper?

apondo yanick profil fotoğrafı

apondo yanick2 yıl önce

@chrdiller You have said it all proff

Ling-Hao (Evan) CHEN profil fotoğrafı

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@chrdiller impressive work!

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New study introduces a system to generate realistic, physically plausible human-object interactions from natural language instructions. It uses LLMs for planning, a motion generator for synchronized body and finger movements, and RL for physical accuracy.

New study introduces a system to generate realistic, physically plausible human-object interactions from natural language instructions. It uses LLMs for planning, a motion generator for synchronized body and finger movements, and RL for physical accuracy.

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Images, video, audio, actions — generative modeling has converged on one recipe: compress into continuous latents, generate in latent space, decode. Language is the lone exception: still generated token by token, as a long discrete stream. Latent Thought Flows: We compress 256 text tokens into 8 continuous latents, generate them with a one-step flow model, and read them out as text using an autoregressive decoder. Result: a better inference-compute vs. generation-quality Pareto frontier than a tuned autoregressive baseline — thanks to compression and one-step generation. Co-led w/ Zhengyang Geng 🧵

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No wonder they say teleoperation is dead Peking University’s DAGroup released HumanNet, a massive 1 million hour dataset of human-centric videos that turns everyday internet footage into gold for embodied AI. It’s got first-person and third-person views, super detailed actions, object interactions, tool use, and long sequences of real behavior. Everything’s cleaned up and annotated with 3D poses, SLAM tracks, and robot-ready labels. The crazy part is,Training on just 1,000 hours of their first-person videos performs as well as (or even slightly better than) 100 hours of actual robot teleop data on downstream tasks. Basically, human videos can now stand in for expensive robot data. Scaling laws for robotics just got a whole lot more realistic. Human priors + smart curation = the future of world models.

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Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

AK

126,595 görüntüleme • 2 yıl önce

Infinite Photorealistic Worlds using Procedural Generation paper page: introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond.

Infinite Photorealistic Worlds using Procedural Generation paper page: introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond.

AK

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On July 11, 1991, during a total solar eclipse, multiple civilians in Mexico City famously filmed a shiny, disk-shaped object hovering in the daytime sky. A TV executive captured the first video from a rooftop, which was broadcast on national television and prompted 40,000+ phone calls and over a dozen other videos from viewers across the city. Witnesses described the object as a bright, metallic “larger object” that “was giving off light” and clearly not a star or plane. The incident is often cited as one of the most documented mass UFO sightings in history, with similar glowing discs recorded simultaneously in cities miles apart.

On July 11, 1991, during a total solar eclipse, multiple civilians in Mexico City famously filmed a shiny, disk-shaped object hovering in the daytime sky. A TV executive captured the first video from a rooftop, which was broadcast on national television and prompted 40,000+ phone calls and over a dozen other videos from viewers across the city. Witnesses described the object as a bright, metallic “larger object” that “was giving off light” and clearly not a star or plane. The incident is often cited as one of the most documented mass UFO sightings in history, with similar glowing discs recorded simultaneously in cities miles apart.

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🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

Tencent Hy

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