Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation abs: paper page:

_akhaliq's profile picture

AK

502,645 subscribers

90,153 görüntüleme • 3 yıl önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

2 Yorum

test bot profil fotoğrafı
test bot3 yıl önce

I'm no expert, but looks like you found the key to some real high-tech animation! Now all you need is a soundtrack 🎵

Georgia Gkioxari profil fotoğrafı
Georgia Gkioxari3 yıl önce

😱

Benzer Videolar

(1/2) Excited to share "Learning Neural Parametric Head Models" #CVPR2023! We capture over 5200 high-quality 3D human head scans from which we build a neural parametric head model that disentangles & expressions and deformations.
0:29

(1/2) Excited to share "Learning Neural Parametric Head Models" #CVPR2023! We capture over 5200 high-quality 3D human head scans from which we build a neural parametric head model that disentangles & expressions and deformations.

Matthias Niessner

53,279 görüntüleme • 3 yıl önce

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior abs: project page:
0:22

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior abs: project page:

AK

163,868 görüntüleme • 3 yıl önce

Champ Controllable and Consistent Human Image Animation with 3D Parametric Guidance In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion
2:10

Champ Controllable and Consistent Human Image Animation with 3D Parametric Guidance In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion

AK

194,356 görüntüleme • 2 yıl önce

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

160,700 görüntüleme • 3 yıl önce

MusicLM: Generating Music From Text Presents MusicLM, a model for generating high-fidelity music from text. MusicLM generates music at 24 kHz that remains consistent over several minutes. proj: abs: data:
0:30

MusicLM: Generating Music From Text Presents MusicLM, a model for generating high-fidelity music from text. MusicLM generates music at 24 kHz that remains consistent over several minutes. proj: abs: data:

Aran Komatsuzaki

163,082 görüntüleme • 3 yıl önce

Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion abs: project page:
1:22

Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion abs: project page:

AK

262,780 görüntüleme • 3 yıl önce

Impressive parametric blisk Tristan built in nTop from this paper: Fricke, K., et al. (2021). Geometry Model and Approach for Future Blisk LCA. IOP Conf. Ser.: Mater. Sci. Eng., 1024, 012067. (
0:22

Impressive parametric blisk Tristan built in nTop from this paper: Fricke, K., et al. (2021). Geometry Model and Approach for Future Blisk LCA. IOP Conf. Ser.: Mater. Sci. Eng., 1024, 012067. (

Bradley Rothenberg

24,137 görüntüleme • 1 yıl önce

BlendFields: Few-Shot Example-Driven Facial Modeling abs: paper page:
4:17

BlendFields: Few-Shot Example-Driven Facial Modeling abs: paper page:

AK

50,038 görüntüleme • 3 yıl önce

DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects abs: paper page:
0:27

DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects abs: paper page:

AK

17,532 görüntüleme • 3 yıl önce

Nvidia and UC San Diego announce What You See is What You GAN Rendering Every Pixel for High-Fidelity Geometry in 3D GANs paper page: scale neural volume rendering to high resolution by rendering every pixel to ensure that "what you see in 2D, is what you get in 3D"
3:11

Nvidia and UC San Diego announce What You See is What You GAN Rendering Every Pixel for High-Fidelity Geometry in 3D GANs paper page: scale neural volume rendering to high resolution by rendering every pixel to ensure that "what you see in 2D, is what you get in 3D"

AK

127,419 görüntüleme • 2 yıl önce

A neck hack for releasing neck tension in 60 seconds! Do not try if you have existing neck issues
1:48

A neck hack for releasing neck tension in 60 seconds! Do not try if you have existing neck issues

Interesting AF

303,505 görüntüleme • 2 ay önce

CosmicMan A Text-to-Image Foundation Model for Humans We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the dilemma of inferior quality and
0:50

CosmicMan A Text-to-Image Foundation Model for Humans We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the dilemma of inferior quality and

AK

46,778 görüntüleme • 2 yıl önce

Introducing Gaze-LLE, a new model for gaze target estimation built on top of a frozen visual foundation model! Gaze-LLE achieves SOTA results on multiple benchmarks while learning minimal parameters, and shows strong generalization paper:
0:27

Introducing Gaze-LLE, a new model for gaze target estimation built on top of a frozen visual foundation model! Gaze-LLE achieves SOTA results on multiple benchmarks while learning minimal parameters, and shows strong generalization paper:

Fiona Ryan

427,067 görüntüleme • 1 yıl önce

1/2 Bring your characters to life like never before! We're officially launching Wan2.2-Animate, a unified model for high-fidelity character animation and replacement. The model weights and inference code are now open-source for the entire community! • Character Animation: Feed it a character image and a reference video, and it will animate your character by precisely replicating the source's expressions and movements. • Character Replacement: Seamlessly swap the animated character into the original video's scene. It automatically matches the lighting and color tone for perfect environmental integration.
1:19

1/2 Bring your characters to life like never before! We're officially launching Wan2.2-Animate, a unified model for high-fidelity character animation and replacement. The model weights and inference code are now open-source for the entire community! • Character Animation: Feed it a character image and a reference video, and it will animate your character by precisely replicating the source's expressions and movements. • Character Replacement: Seamlessly swap the animated character into the original video's scene. It automatically matches the lighting and color tone for perfect environmental integration.

Wan

4,522,567 görüntüleme • 8 ay önce

We relived Michelangelo in neural network style!! Super excited to finally share Neuralangelo 🛠 -- 3D surface reconstruction from RGB videos with extremely high fidelity! 🤩 To appear at #CVPR2023 project page: paper: 🧵⬇️ (1/4)
0:24

We relived Michelangelo in neural network style!! Super excited to finally share Neuralangelo 🛠 -- 3D surface reconstruction from RGB videos with extremely high fidelity! 🤩 To appear at #CVPR2023 project page: paper: 🧵⬇️ (1/4)

Chen-Hsuan Lin

253,341 görüntüleme • 3 yıl önce

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning investigated the application of Deep Reinforcement Learning (Deep RL) for low-cost, miniature humanoid hardware in a dynamic environment, showing the method can synthesize sophisticated and safe movement skills making up complex behavioral strategies in a simplified one-versus-one (1v1) soccer game abs: project page:
4:28

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning investigated the application of Deep Reinforcement Learning (Deep RL) for low-cost, miniature humanoid hardware in a dynamic environment, showing the method can synthesize sophisticated and safe movement skills making up complex behavioral strategies in a simplified one-versus-one (1v1) soccer game abs: project page:

AK

293,261 görüntüleme • 3 yıl önce

MetaHuman Animator is here to help you unlock the storytelling power of high-fidelity facial animation. From performance to animation in minutes, with just an iPhone and a PC! It's available now! Learn more:
0:50

MetaHuman Animator is here to help you unlock the storytelling power of high-fidelity facial animation. From performance to animation in minutes, with just an iPhone and a PC! It's available now! Learn more:

Unreal Engine

246,938 görüntüleme • 3 yıl önce

⚡ Anime.js A lightweight animation library that makes motion simple and expressive. Perfect for learning animation timing, easing, and sequencing and for pairing with #threejs to animate 3D scenes smoothly. 👉 creator: Julian Garnier #animejs WebGPU #webgl #animation #frontend #learning
0:47

⚡ Anime.js A lightweight animation library that makes motion simple and expressive. Perfect for learning animation timing, easing, and sequencing and for pairing with #threejs to animate 3D scenes smoothly. 👉 creator: Julian Garnier #animejs WebGPU #webgl #animation #frontend #learning

Three.js Resources

54,236 görüntüleme • 4 ay önce

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:
0:32

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

Chuang Gan

43,265 görüntüleme • 1 yıl önce