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🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ Jiajun Wu & Karen 💡 How? We reformulate 3D motion estimation as generating consistent multi-view 2D pose... show more

Jiaman Li

1,471 subscribers

15,886 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Shashwat

Shashwatvor 1 Jahr

@jiajunwu_cs how would it differentiate between a baby crawling and a dog crawling ? very creative though 👍

Profilbild von Jiaman Li

Jiaman Livor 1 Jahr

@jiajunwu_cs It requires training on specific types of 2D keypoints.

Profilbild von Digital Currency

Digital Currencyvor 2 Jahren

From 3D modeling to VR/AR development, our MSc in Metaverse program equips you with the technical skills to excel in the rapidly evolving digital world. Don't miss out—enroll today! #UNIC #MScMetaverse

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161,530 Aufrufe • vor 2 Jahren

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I’ve dreamt of creating a tool that could animate anyone with any motion from just ONE image… and now it’s a reality! 🎉 Super excited to introduce updated 3DHM: Synthesizing Moving People with 3D Control. 🕺💃3DHM can generate human videos from a single real or synthetic human image. #Animation #GenAI #AI #3DHM ✨ The magic of 3D control? Turning 2D pixels into lifelike, animated humans. 🎥 Check out our demo (and Merry Christmas)! Paper: Github: Webpage: Proudly working with the great Junming (Leo) Chen, , Yossi Gandelsman, Alyosha Efros and Jitendra MALIK😃 Kindly note: This video is intended solely for research purposes and is not authorized for commercial use.

Boyi Li

52,482 Aufrufe • vor 1 Jahr

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Tracking Everything Everywhere All at Once paper page: present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajectories. We propose a complete and globally consistent motion representation, dubbed OmniMotion, that allows for accurate, full-length motion estimation of every pixel in a video. OmniMotion represents a video using a quasi-3D canonical volume and performs pixel-wise tracking via bijections between local and canonical space. This representation allows us to ensure global consistency, track through occlusions, and model any combination of camera and object motion. Extensive evaluations on the TAP-Vid benchmark and real-world footage show that our approach outperforms prior state-of-the-art methods by a large margin both quantitatively and qualitatively.

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280,547 Aufrufe • vor 3 Jahren

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327,105 Aufrufe • vor 2 Jahren

Tencent's Hunyuan 3D Studio is LIVE! 🎨 AI-powered 3D creation for professionals, slashing workflows from days to minutes.⚡️ Key Features: 🔹Text-to-3D: Generate controllable geometry (multi-view, multi-style, A pose, bbox-control) from text/images. 🔹Auto Part Split: Break models into 50+ editable components (hats, clothes, etc.) 🔹1-Min UV Unwrap: AI-driven, professional-grade UVs, no more manual fixes. 🔹PBR Textures: Precisely edit global or local textures with text or image prompts & professional material sphere generation. 🔹Auto Rigging: Quickly rig various characters, with specialized motion templates for human models. Hunyuan3D Studio is built to empower game developers, animators, and designers to create faster and smarter. 📄Tech Report: 👉🏻Try it now:

Tencent's Hunyuan 3D Studio is LIVE! 🎨 AI-powered 3D creation for professionals, slashing workflows from days to minutes.⚡️ Key Features: 🔹Text-to-3D: Generate controllable geometry (multi-view, multi-style, A pose, bbox-control) from text/images. 🔹Auto Part Split: Break models into 50+ editable components (hats, clothes, etc.) 🔹1-Min UV Unwrap: AI-driven, professional-grade UVs, no more manual fixes. 🔹PBR Textures: Precisely edit global or local textures with text or image prompts & professional material sphere generation. 🔹Auto Rigging: Quickly rig various characters, with specialized motion templates for human models. Hunyuan3D Studio is built to empower game developers, animators, and designers to create faster and smarter. 📄Tech Report: 👉🏻Try it now:

Tencent Hy

46,892 Aufrufe • vor 10 Monaten

📢📢 𝐏𝐫𝐄𝐝𝐢𝐭𝐨𝐫𝟑𝐃: 𝐅𝐚𝐬𝐭 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐞 𝟑𝐃 𝐒𝐡𝐚𝐩𝐞 𝐄𝐝𝐢𝐭𝐢𝐧𝐠 📢📢 We propose a training-free 3D shape editing approach that rapidly and precisely edits the regions intended by the user and keeps the rest as is. Using a quickly brushed mask and a text prompt, we first apply multi-view editing in the 2D domain and then run our merging algorithm in the 3D feature space to ensure that the edited shape is loyal to the input shape. Project Page: Video: Great work by Ziya Erkoç Can Gümeli Chaoyang Wang Angela Dai Peter Wonka Hsin-Ying Lee Peiye Zhuang

📢📢 𝐏𝐫𝐄𝐝𝐢𝐭𝐨𝐫𝟑𝐃: 𝐅𝐚𝐬𝐭 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐞 𝟑𝐃 𝐒𝐡𝐚𝐩𝐞 𝐄𝐝𝐢𝐭𝐢𝐧𝐠 📢📢 We propose a training-free 3D shape editing approach that rapidly and precisely edits the regions intended by the user and keeps the rest as is. Using a quickly brushed mask and a text prompt, we first apply multi-view editing in the 2D domain and then run our merging algorithm in the 3D feature space to ensure that the edited shape is loyal to the input shape. Project Page: Video: Great work by Ziya Erkoç Can Gümeli Chaoyang Wang Angela Dai Peter Wonka Hsin-Ying Lee Peiye Zhuang

Matthias Niessner

13,097 Aufrufe • vor 1 Jahr

Seamless Human Motion Composition with Blended Positional Encodings Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.

Seamless Human Motion Composition with Blended Positional Encodings Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.

AK

30,864 Aufrufe • vor 2 Jahren

Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

AK

106,527 Aufrufe • vor 3 Jahren