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"A Generative Motion Rig for Artist Driven Motion Authoring" from Disney Research Zurich and ETH Zurich So cool! Full video: Paper:

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Chris Offner

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37,118 görüntüleme • 7 gün önce •via X (Twitter)

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Synthesia research releases HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints project page: paper page:
3:06

Synthesia research releases HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints project page: paper page:

AK

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Example-based Motion Synthesis via Generative Motion Matching paper page: present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.
5:56

Example-based Motion Synthesis via Generative Motion Matching paper page: present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.

AK

93,729 görüntüleme • 3 yıl önce

A new paper by Disney Research introduces a 2-stage technique for motion tracking in physics-based character animation, using a pretrained latent space and RL to control diverse and unseen motions on both virtual and real-world humanoid robots. Paper:
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A new paper by Disney Research introduces a 2-stage technique for motion tracking in physics-based character animation, using a pretrained latent space and RL to control diverse and unseen motions on both virtual and real-world humanoid robots. Paper:

The Humanoid Hub

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Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI
3:58

Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI

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Transformer robot is now opening doors for you! ETH Zürich @swiss_mile D-MAVT, ETH Zurich Full video: Authors: Clemens Schwarke, Victor Klemm, Matthijs van der Boon, Marko Bjelonic, and Marco Hutter #robotics #ai #reinforcementlearning #ethzurich
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Transformer robot is now opening doors for you! ETH Zürich @swiss_mile D-MAVT, ETH Zurich Full video: Authors: Clemens Schwarke, Victor Klemm, Matthijs van der Boon, Marko Bjelonic, and Marco Hutter #robotics #ai #reinforcementlearning #ethzurich

Robotic Systems Lab

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From live video to 3D avatar in seconds. This #SIGGRAPH2025 paper from Adobe Research reconstructs a realistic head avatar on the fly, with no pre-cached data, and adapts seamlessly to facial motion for VR, animation, and online communication. 🔗
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From live video to 3D avatar in seconds. This #SIGGRAPH2025 paper from Adobe Research reconstructs a realistic head avatar on the fly, with no pre-cached data, and adapts seamlessly to facial motion for VR, animation, and online communication. 🔗

Adobe Research

29,083 görüntüleme • 11 ay önce

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.
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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

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This research from ETH Zurich examines advancements in quadruped robots for industrial settings, specifically addressing their ability to climb ladders—a previously overlooked but essential capability. While legged robots excel over wheeled robots on rough terrain, their inability to scale ladders has limited their effectiveness in inspecting dangerous areas, impacting both safety and efficiency. Credit: ETH Zurich #engineering #technology #robotics #robots -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.
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This research from ETH Zurich examines advancements in quadruped robots for industrial settings, specifically addressing their ability to climb ladders—a previously overlooked but essential capability. While legged robots excel over wheeled robots on rough terrain, their inability to scale ladders has limited their effectiveness in inspecting dangerous areas, impacting both safety and efficiency. Credit: ETH Zurich #engineering #technology #robotics #robots -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.

Wevolver

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What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000+ motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: Code: Paper: (ACM TOG / SIGGRAPH 2026)
6:25

What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000+ motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: Code: Paper: (ACM TOG / SIGGRAPH 2026)

tingwu.wang

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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.
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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

125,319 görüntüleme • 3 yıl önce

Generative video is dope but AI motion capture is just as cool. Pretty much James Cameron ish in your living room — and that too with just a 2D camera
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Generative video is dope but AI motion capture is just as cool. Pretty much James Cameron ish in your living room — and that too with just a 2D camera

Bilawal Sidhu

89,178 görüntüleme • 1 yıl önce

Full video of Melanie Martinez breaking down the meaning of “DISNEY PRINCESS” in a new video for Genius. 💛 Posted early on Daily Motion.
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Full video of Melanie Martinez breaking down the meaning of “DISNEY PRINCESS” in a new video for Genius. 💛 Posted early on Daily Motion.

nis

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🎙️ FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait 👄 Jupyter Notebook 🥳 Thanks to Taekyung Ki ❤ DongchanMin ❤ Gyeongsu Chae ❤ Thanks to Baku ❤ for the tutorial. 🌐page: 🧬code: 📄paper: 🍊jupyter:
1:04

🎙️ FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait 👄 Jupyter Notebook 🥳 Thanks to Taekyung Ki ❤ DongchanMin ❤ Gyeongsu Chae ❤ Thanks to Baku ❤ for the tutorial. 🌐page: 🧬code: 📄paper: 🍊jupyter:

camenduru

12,363 görüntüleme • 1 yıl önce

We wrote a paper: estimating body and hand motion from a pair of glasses! 👓 website: code:
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We wrote a paper: estimating body and hand motion from a pair of glasses! 👓 website: code:

Brent Yi

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Diamond League Finals are cool and all, but nothing in Europe can quite match the aura of a good train in eyes of Graham Blanks 🚂 FULL INTERVIEWS FROM THE ZURICH DIAMOND LEAGUE:
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Diamond League Finals are cool and all, but nothing in Europe can quite match the aura of a good train in eyes of Graham Blanks 🚂 FULL INTERVIEWS FROM THE ZURICH DIAMOND LEAGUE:

CITIUS MAG

11,671 görüntüleme • 10 ay önce

byebye expensive motion tracking equipment 👋 ai makes motion capturing so easy now! nvidia presented GENMO last week, a new model that can generate and estimate human motion from text, audio, video, and 3D keyframes
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byebye expensive motion tracking equipment 👋 ai makes motion capturing so easy now! nvidia presented GENMO last week, a new model that can generate and estimate human motion from text, audio, video, and 3D keyframes

Dreaming Tulpa 🥓👑

73,693 görüntüleme • 1 yıl önce

we’re launching Motion, the frontier agent for tasteful motion design. this launch video is made entirely with Motion. 👇🏽 QT + comment "MOTION" to get 1,000 free credits. tag Motion in any X post for a surprise. now generally available at motion [dot] so.
1:20

we’re launching Motion, the frontier agent for tasteful motion design. this launch video is made entirely with Motion. 👇🏽 QT + comment "MOTION" to get 1,000 free credits. tag Motion in any X post for a surprise. now generally available at motion [dot] so.

Adish Jain ☕️

932,817 görüntüleme • 1 ay önce

I dont know who made this Video, but this is one of the best, from Zurich and for sure from "Waiting for the End" ❤️❤️
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I dont know who made this Video, but this is one of the best, from Zurich and for sure from "Waiting for the End" ❤️❤️

Linkin Park Shows !

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today, we’re launching a new way to interact with Motion. no more prompting & praying. just talk. start with a simple message like "yo" or "what's trending on tech twitter today?" Motion can do research, suggest assets, pull context from GitHub & Notion, propose ideas, and refine the creative direction with you. this launch video is made entirely with Motion. 👇🏽 now available at motion [dot] so.
0:45

today, we’re launching a new way to interact with Motion. no more prompting & praying. just talk. start with a simple message like "yo" or "what's trending on tech twitter today?" Motion can do research, suggest assets, pull context from GitHub & Notion, propose ideas, and refine the creative direction with you. this launch video is made entirely with Motion. 👇🏽 now available at motion [dot] so.

Adish Jain ☕️

19,601 görüntüleme • 15 gün önce

Want to track features in high-speed motion? Check out our #CVPR2023 paper and Award candidate, which introduces the first data-driven feature tracker for #eventcameras Paper, code: Kudos Messikommer Carter Mathias Gehrig
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Want to track features in high-speed motion? Check out our #CVPR2023 paper and Award candidate, which introduces the first data-driven feature tracker for #eventcameras Paper, code: Kudos Messikommer Carter Mathias Gehrig

Davide Scaramuzza

24,032 görüntüleme • 3 yıl önce