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Interested in upgrading your Motion Matching system? Check out our #SIGGRAPH2024 work where we introduce *Codebook Matching* to train neural character controllers for synthesizing lifelike embodied animations in #Games and #VR! Project Page:

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Sebastian Starke

3,310 subscribers

18,645 次观看 • 1 年前 •via X (Twitter)

科学技术#SIGGRAPH2024#Games#VR
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4 条评论

Paul Starke 的头像
Paul Starke1 年前

Paper can be found here

Zhiyang (Frank) Dou 的头像
Zhiyang (Frank) Dou1 年前

Cool work!

Michael Gold 的头像
Michael Gold1 年前

Awesome work. Congrats!

Andrew Carr (e/🤸) 的头像
Andrew Carr (e/🤸)1 年前

Very cool work!

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The code, dataset and demos for our #SIGGRAPH2024 work on generating embodied avatar movements with Codebook Matching are now available! Everything can be found on the project page, including a precompiled binary that you can play around with!
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The code, dataset and demos for our #SIGGRAPH2024 work on generating embodied avatar movements with Codebook Matching are now available! Everything can be found on the project page, including a precompiled binary that you can play around with!

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Remember we promised you a game animation sample at GDC 2024? It’s here! Get up and running with Motion Matching in this project that includes hundreds of free AAA-quality animations 🏃‍♂️
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Remember we promised you a game animation sample at GDC 2024? It’s here! Get up and running with Motion Matching in this project that includes hundreds of free AAA-quality animations 🏃‍♂️

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Motion Matching based locomotion system in #UE5 (Gameplay ready & Fully Replicated)
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Motion Matching based locomotion system in #UE5 (Gameplay ready & Fully Replicated)

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Fortnite has improved the animations on ALL platforms! "Motion Matching and Procedural Layering are features that result in improved animations for things like transitioning from walking to running, changing directions, and using a weapon."
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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:
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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:

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David Pusher released RDR System, a C++ project that integrates ALS and GASP with Motion Matching, along with seamless switching between third- and first-person perspectives. Get it for free:
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David Pusher released RDR System, a C++ project that integrates ALS and GASP with Motion Matching, along with seamless switching between third- and first-person perspectives. Get it for free:

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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.
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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.

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Excited to share our #NeurIPS2025 work on learning motion hierarchies! We introduce a general hierarchical graph learning method that learns structured, interpretable motion directly from data, no prior structure or assumptions needed!!! Project and Paper: Amazing work led by William Koch, Cheng Zheng, and Baiang Li ! See us in San Diego for #NeurIPS2025!

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Last week, we talked to the team at White Paper Games about how they utilized Unreal Engine 5’s Motion Matching, Control Rig and Sequencer to create a gritty, immersive, nail-biting thriller in I AM RIPPER. Explore the process behind the player-driven, branching narrative and how it game together in our article:

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✨We are excited to open-source Tencent HY-Motion 1.0, a billion-parameter text-to-motion model built on the Diffusion Transformer (DiT) architecture and flow matching. Tencent HY-Motion 1.0 empowers developers and individual creators alike by transforming natural language into high-fidelity, fluid, and diverse 3D character animations, delivering exceptional instruction-following capabilities across a broad range of categories. The generated 3D animation assets can be seamlessly integrated into typical 3D animation pipelines.🎮🎥 Highlights: 🔹Billion-Scale DiT: Successfully scaled flow-matching DiT to 1B+ parameters, setting a new ceiling for instruction-following capability and generated motion quality. 🔹Full-Stage Training Strategy: The industry’s first motion generation model featuring a complete Pre-training → SFT → RL loop to optimize physical plausibility and semantic accuracy. 🔹Comprehensive Category Coverage: Features 200+ motion categories across 6 major classes—the most comprehensive in the industry, curated via a meticulous data pipeline. 🌐Project Page: 🔗Github: 🤗Hugging Face: 📄Technical report:

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327,984 次观看 • 5 个月前