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Adobe presents SynCHMR! A method that can reconstruct camera trajectory, human motion, and scene in one global coordinate from videos 🔥

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Dreaming Tulpa 🥓👑

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108,620 görüntüleme • 2 yıl önce •via X (Twitter)

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If you like Tweets like this, you might enjoy my weekly newsletter, #aiartweekly. A free, once–weekly e-mail round-up of the latest AI art news, interviews with artists and useful tools & resources. Join 3500+ subscribers here:

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Jules revenue-driven SEO2 yıl önce

What is the use case of this?

Chad profil fotoğrafı
Chad2 yıl önce

Code or demo coming soon I hope??

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Steve Korshakov2 yıl önce

no code?

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Yacine Mahdid2 yıl önce

100% thought grandma was about to do some fire parkour

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Emīls Geršinskis2 yıl önce

Adobe is cool only on paper

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toni2 yıl önce

is this another version of EasyMocap ? utilizing SMPL-X ?

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Bye bye expensive mocap equipment 👋 AiOS is yet another method that can reconstruct human from videos and images! This one also supports hands and facial expressions.
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Bye bye expensive mocap equipment 👋 AiOS is yet another method that can reconstruct human from videos and images! This one also supports hands and facial expressions.

Dreaming Tulpa 🥓👑

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adobe just released a method that can generate transparent videos from text and images 👀
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adobe just released a method that can generate transparent videos from text and images 👀

Dreaming Tulpa 🥓👑

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Researchers presented Vid2Avatar, a method that creates human avatars from in-the-wild videos via self-supervised scene decomposition. See more: #AI #ArtificialIntelligence #3dart #research
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Researchers presented Vid2Avatar, a method that creates human avatars from in-the-wild videos via self-supervised scene decomposition. See more: #AI #ArtificialIntelligence #3dart #research

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New Adobe Premiere plug-in that edits podcasts and multi-camera videos automatically
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New Adobe Premiere plug-in that edits podcasts and multi-camera videos automatically

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Let's Check In On MIT's ONE TRILLION FPS (Frames Per Second) Camera. A Camera That Can Film Light In Motion.
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Let's Check In On MIT's ONE TRILLION FPS (Frames Per Second) Camera. A Camera That Can Film Light In Motion.

🔥⭐️Edwin⭐️🔥

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Can we hear what's inside your head? 🧠→🎶 Our new paper in PLOS Biology, led by Jong-Yun Park, presents an AI-based method to reconstruct arbitrary natural sounds directly from a person's brain activity measured with fMRI.
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Can we hear what's inside your head? 🧠→🎶 Our new paper in PLOS Biology, led by Jong-Yun Park, presents an AI-based method to reconstruct arbitrary natural sounds directly from a person's brain activity measured with fMRI.

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new Loopy model from ByteDance can generate whole videos of realistic face motion from just ONE IMAGE and a SOUND getting that feeling again...
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new Loopy model from ByteDance can generate whole videos of realistic face motion from just ONE IMAGE and a SOUND getting that feeling again...

the real deepfates

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Meta presents Garment3DGen! A method that can stylize the geometry and textures from 2D image and 3D mesh garments, which can be fitted on top of parametric bodies and be simulated 🔥 More examples ⬇️
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Meta presents Garment3DGen! A method that can stylize the geometry and textures from 2D image and 3D mesh garments, which can be fitted on top of parametric bodies and be simulated 🔥 More examples ⬇️

Dreaming Tulpa 🥓👑

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adobe is cooking a new method that can automatically rig 3D assets by generating joints, skeletons, and skinning weights 👀
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adobe is cooking a new method that can automatically rig 3D assets by generating joints, skeletons, and skinning weights 👀

Dreaming Tulpa 🥓👑

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Want a robot that learns household tasks by watching you? EquiBot is a ✨ generalizable and 🚰 data-efficient method for visuomotor policy learning, robust to changes in object shapes, lighting, and scene makeup, even from just 5 mins of human videos. 🧵↓
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Want a robot that learns household tasks by watching you? EquiBot is a ✨ generalizable and 🚰 data-efficient method for visuomotor policy learning, robust to changes in object shapes, lighting, and scene makeup, even from just 5 mins of human videos. 🧵↓

Jingyun Yang

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🤖Simulation-Ready Human–Scene Interaction Reconstruction🤖 #HSImul3R reconstructs sim-ready 3D human-scene interactions from casual videos with *physics-in-the-loop* that can be directly deployed to humanoids. - Project: - Code:
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🤖Simulation-Ready Human–Scene Interaction Reconstruction🤖 #HSImul3R reconstructs sim-ready 3D human-scene interactions from casual videos with *physics-in-the-loop* that can be directly deployed to humanoids. - Project: - Code:

Ziwei Liu

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Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.
1:44

Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.

The Humanoid Hub

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Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes Contributions: • We propose STORM, the first feed-forward, self-supervised method for fast and accurate reconstruction of dynamic 3D scenes from sparse, multi-timestep, posed camera images. • Our bottom-up framework aggregates and transforms per-frame 3D Gaussian Splats into a cohesive scene representation, enabling self-supervised motion estimation. Furthermore, we introduce motion tokens that capture common motion primitives and regularize motion predictions, facilitating dynamic motion group segmentation without explicit motion or correspondence supervision. • We present several enhancements for in-the-wild scenarios, including sky modeling, camera exposure inconsistency handling, large novel-view extrapolation, and fine-grained human motions reconstruction, making STORM well-suited for real-world applications.
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Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes Contributions: • We propose STORM, the first feed-forward, self-supervised method for fast and accurate reconstruction of dynamic 3D scenes from sparse, multi-timestep, posed camera images. • Our bottom-up framework aggregates and transforms per-frame 3D Gaussian Splats into a cohesive scene representation, enabling self-supervised motion estimation. Furthermore, we introduce motion tokens that capture common motion primitives and regularize motion predictions, facilitating dynamic motion group segmentation without explicit motion or correspondence supervision. • We present several enhancements for in-the-wild scenarios, including sky modeling, camera exposure inconsistency handling, large novel-view extrapolation, and fine-grained human motions reconstruction, making STORM well-suited for real-world applications.

MrNeRF

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Nvidia presents DreamDojo A Generalist Robot World Model from Large-Scale Human Videos
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Nvidia presents DreamDojo A Generalist Robot World Model from Large-Scale Human Videos

AK

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📢📢📢 Excited to share our new work *Autonomous Character-Scene Interaction Synthesis from Text Instruction* (Siggraph Asia 24). It presents a unified model for flexible scene-conditioned motion generation given text, scene, trajectory conditions. The results with smooth interaction look very impressive! 📰Paper: Project: Code and data will be released soon.
6:34

📢📢📢 Excited to share our new work *Autonomous Character-Scene Interaction Synthesis from Text Instruction* (Siggraph Asia 24). It presents a unified model for flexible scene-conditioned motion generation given text, scene, trajectory conditions. The results with smooth interaction look very impressive! 📰Paper: Project: Code and data will be released soon.

Siyuan Huang

11,340 görüntüleme • 1 yıl önce

Motion cues from human videos + Reasoning from VLMs enables 3D hand trajectory prediction in-the-wild for novel tasks in novel scenes! Very excited to share the EgoMan project, a huge effort led by Mingfei Chen during an internship with us at Meta. Video summary ⬇️
3:17

Motion cues from human videos + Reasoning from VLMs enables 3D hand trajectory prediction in-the-wild for novel tasks in novel scenes! Very excited to share the EgoMan project, a huge effort led by Mingfei Chen during an internship with us at Meta. Video summary ⬇️

Homanga Bharadhwaj @ CVPR

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