Xiao-Xiao Long's banner

Xiao-Xiao Long

@xxlong0 • 1,248 subscribers

Associate Professor @ NJU. | Organizer of @anysyn3d Spatial Intelligence & Robotics.

Shorts

Thanks for the tweet. We improve 3D Gaussain splatting for dash cam videos that contain serious reflections and obstructions. With Dash cam videos, it's possible to leverage massive driving data. Visit our page at Codes will be soon.

Thanks for the tweet. We improve 3D Gaussain splatting for dash cam videos that contain serious reflections and obstructions. With Dash cam videos, it's possible to leverage massive driving data. Visit our page at Codes will be soon.

44,444 просмотров

An image + a detailed normal map generated by GeoWizard --> a relightable image. Geowizard is a generative geometry estimation model that could jointly produce high-quality depth and normal with intricate details. Try our huggingface demo here: #huggingface #gradio #geowizard

An image + a detailed normal map generated by GeoWizard --> a relightable image. Geowizard is a generative geometry estimation model that could jointly produce high-quality depth and normal with intricate details. Try our huggingface demo here: #huggingface #gradio #geowizard

30,338 просмотров

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Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:

Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:

24,177 просмотров • 3 месяцев назад

Extract accurate depth from your 3D gaussians! Fast, accurate, and easy to use. Code of RaDe-GS is released! project page: code: paper: No need to change your 3D GS representations, we propose a play-and-plug rasterizer to extract accurate depth and normal from 3D gaussians, leading to the possibility of high-quality geometry reconstruction. It's quite efficient and fast, achieving 0.69mm errors on DTU dataset in only 5 minutes. Very easy to adopt the rasterizer in your any 3dgs projects.

Extract accurate depth from your 3D gaussians! Fast, accurate, and easy to use. Code of RaDe-GS is released! project page: code: paper: No need to change your 3D GS representations, we propose a play-and-plug rasterizer to extract accurate depth and normal from 3D gaussians, leading to the possibility of high-quality geometry reconstruction. It's quite efficient and fast, achieving 0.69mm errors on DTU dataset in only 5 minutes. Very easy to adopt the rasterizer in your any 3dgs projects.

31,290 просмотров • 2 лет назад

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