Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

🎉 Our paper "PGC: Physics-Based Gaussian Cloth from a Single Pose" has been accepted to #CVPR2025! 👕 PGC uses a PBR + 3DGS representation to render simulation-ready garments under novel lighting and motion, all from a single static frame. ✨Web: 🧵1/4

Michelle Guo

1,178 subscribers

10,795 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.

Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.

MrNeRF

27,277 views • 1 year ago

PhysX-Anything: creates simulation-ready, articulated 3D assets from a single image; VLM-based model using a new 3D representation that reduces token count by 193x

PhysX-Anything: creates simulation-ready, articulated 3D assets from a single image; VLM-based model using a new 3D representation that reduces token count by 193x

Wildminder

49,610 views • 6 months ago

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

AK

20,651 views • 2 years ago

VAST AI releases Triplane Meets Gaussian Splatting on Hugging Face Fast and Generalizable Single-View 3D Reconstruction with Transformers demo: TGS enables fast reconstruction from single-view image. It builds the 3D representation upon a hybrid Triplane-Gaussian representation by evaluating a transformer-based framework, from which 3D Gaussians would be decoded

VAST AI releases Triplane Meets Gaussian Splatting on Hugging Face Fast and Generalizable Single-View 3D Reconstruction with Transformers demo: TGS enables fast reconstruction from single-view image. It builds the 3D representation upon a hybrid Triplane-Gaussian representation by evaluating a transformer-based framework, from which 3D Gaussians would be decoded

AK

113,220 views • 2 years ago

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

Yuan Liu

79,592 views • 1 year ago

Powerful pictorial representation #PGC

Powerful pictorial representation #PGC

khadija siddiqi

210,771 views • 1 year ago

Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:

Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:

Bernhard Kerbl

356,188 views • 2 years ago

1/ Announcing Enso V2 Navigate all of DeFi from a single dashboard, with a single click 🧵 👇

1/ Announcing Enso V2 Navigate all of DeFi from a single dashboard, with a single click 🧵 👇

Enso | ⌘ 🛠️

45,555 views • 3 years ago

PHC has been accepted by ICCV 2023! We aim to develop a physics-based humanoid controller capable of imitating ALL of the motion from the AMASS dataset (almost there🧐), recover from failure state, does NOT use any external forces, all the while supporting real-time use cases!

PHC has been accepted by ICCV 2023! We aim to develop a physics-based humanoid controller capable of imitating ALL of the motion from the AMASS dataset (almost there🧐), recover from failure state, does NOT use any external forces, all the while supporting real-time use cases!

Zhengyi “Zen” Luo

57,938 views • 2 years ago

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗨𝗩𝗚𝗦 We introduce 𝗨𝗩𝗚𝗦, a new 2D representation of 3D Gaussian Splatting (3DGS) that leverages spherical mapping. Website: Paper:

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗨𝗩𝗚𝗦 We introduce 𝗨𝗩𝗚𝗦, a new 2D representation of 3D Gaussian Splatting (3DGS) that leverages spherical mapping. Website: Paper:

Nikolaos Sarafianos

10,707 views • 1 year ago

Paper rejected from #CVPR2025, paper ready for #ICCV2025 💪

Paper rejected from #CVPR2025, paper ready for #ICCV2025 💪

Kosta Derpanis

787,989 views • 1 year ago

THE CURSE HAS BEEN BROKEN WE CLAIM OUR FIRST #TSMWIN OF PGC 2024

THE CURSE HAS BEEN BROKEN WE CLAIM OUR FIRST #TSMWIN OF PGC 2024

TSM

20,221 views • 1 year ago

Sudden Miyeon confession time ✨ IU has been known to make girls blush, melt from eye contact and eyes wavering from a single look

Sudden Miyeon confession time ✨ IU has been known to make girls blush, melt from eye contact and eyes wavering from a single look

Strawberry Moon

31,796 views • 1 year ago

🏗️ Build #3D scenes from a single input view based on the latest #NVIDIAResearch paper accepted at #ICCV2023. 👀 This 3D-aware #generativeAI model can build diverse and plausible views. research paper: project page:

🏗️ Build #3D scenes from a single input view based on the latest #NVIDIAResearch paper accepted at #ICCV2023. 👀 This 3D-aware #generativeAI model can build diverse and plausible views. research paper: project page:

NVIDIA AI Developer

113,873 views • 2 years ago

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

MrNeRF

17,759 views • 1 year ago

IllumiNeRF 3D Relighting without Inverse Rendering Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination

IllumiNeRF 3D Relighting without Inverse Rendering Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination

AK

12,369 views • 2 years ago

Hundreds of full web apps, all rendered live on a single canvas, delivering the same performance you’d expect from static images. Coming soon to the best design tool near you.

Hundreds of full web apps, all rendered live on a single canvas, delivering the same performance you’d expect from static images. Coming soon to the best design tool near you.

Pietro Schirano

50,431 views • 1 year ago

New paper from Apple - Sharp Monocular View Synthesis in Less than a Second Mescheder et al. @ Apple just released a very impressive paper (congrats! 🎉🥳). You give it an image and it generates a really great looking 3d Gaussian representation. Uses depth pro. It's really good. The model is about 3GB, and takes ~5-10s on my M1 Max per image. Single feedforward pass network. The video is using "Metal Splatter" to view the .ply from ml-sharp on Apple Vision Pro. Some really wow moments today trying it on different scenes.

New paper from Apple - Sharp Monocular View Synthesis in Less than a Second Mescheder et al. @ Apple just released a very impressive paper (congrats! 🎉🥳). You give it an image and it generates a really great looking 3d Gaussian representation. Uses depth pro. It's really good. The model is about 3GB, and takes ~5-10s on my M1 Max per image. Single feedforward pass network. The video is using "Metal Splatter" to view the .ply from ml-sharp on Apple Vision Pro. Some really wow moments today trying it on different scenes.

Tim Davison ᯅ

132,711 views • 6 months ago

What happens if you try to push and squash a Gaussian splat? :) Been playing with adding simplified deformation and a lightweight physics simulation layer to Gaussian splats, running directly in real time on mobile. Instead of simulating every splat, this uses a small CPU lattice to drive the deformation, plus a per-splat transform so they actually stretch and compress properly. No mesh conversion, no retraining. Just layered on top of the renderer. You can push, bend, squash a scan and it holds together surprisingly well... Still early, but I can see the potential in making splats feel more real, from static captures to something more tactile, something you can touch, throw, bounce around… or well just fold and squash on the floor :) #GaussianSplatting #3DGS #Unity

What happens if you try to push and squash a Gaussian splat? :) Been playing with adding simplified deformation and a lightweight physics simulation layer to Gaussian splats, running directly in real time on mobile. Instead of simulating every splat, this uses a small CPU lattice to drive the deformation, plus a per-splat transform so they actually stretch and compress properly. No mesh conversion, no retraining. Just layered on top of the renderer. You can push, bend, squash a scan and it holds together surprisingly well... Still early, but I can see the potential in making splats feel more real, from static captures to something more tactile, something you can touch, throw, bounce around… or well just fold and squash on the floor :) #GaussianSplatting #3DGS #Unity

𝐑𝐔𝐁𝐄𝐍🥽𝐅𝐑𝐎

50,905 views • 2 months ago

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:

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

160,700 views • 3 years ago