Yash Bhalgat
@ysbhalgat.bsky.social
PhD at VGG, Oxford w/ Andrew Zisserman, Andrea Vedaldi, Joao Henriques, Iro Laina. Past: Senior RS Qualcomm #AI #Research, UMich, IIT Bombay.
I occasionally post AI memes.
yashbhalgat.github.io
I occasionally post AI memes.
yashbhalgat.github.io
Need to rig 3D models? 🦖
New work from UCSD and Adobe:
"RigAnything: Template-Free Autoregressive Rigging
for Diverse 3D Assets" Liu et al.
tl;dr: reduces rigging time from 2 mins to 2 secs, works on any shape category & doesn't need predefined templates! 🚀
New work from UCSD and Adobe:
"RigAnything: Template-Free Autoregressive Rigging
for Diverse 3D Assets" Liu et al.
tl;dr: reduces rigging time from 2 mins to 2 secs, works on any shape category & doesn't need predefined templates! 🚀
February 15, 2025 at 1:05 PM
Need to rig 3D models? 🦖
New work from UCSD and Adobe:
"RigAnything: Template-Free Autoregressive Rigging
for Diverse 3D Assets" Liu et al.
tl;dr: reduces rigging time from 2 mins to 2 secs, works on any shape category & doesn't need predefined templates! 🚀
New work from UCSD and Adobe:
"RigAnything: Template-Free Autoregressive Rigging
for Diverse 3D Assets" Liu et al.
tl;dr: reduces rigging time from 2 mins to 2 secs, works on any shape category & doesn't need predefined templates! 🚀
"Latent Radiance Fields with 3D-aware 2D Representations" Zhou et al., #ICLR2025
tl;dr: Novel framework that integrates 3D awareness into VAE latent space using correspondence-aware encoding, enabling high-quality rendered images with ~50% memory savings.
(1/n) 🧵
tl;dr: Novel framework that integrates 3D awareness into VAE latent space using correspondence-aware encoding, enabling high-quality rendered images with ~50% memory savings.
(1/n) 🧵
February 14, 2025 at 10:28 AM
"Latent Radiance Fields with 3D-aware 2D Representations" Zhou et al., #ICLR2025
tl;dr: Novel framework that integrates 3D awareness into VAE latent space using correspondence-aware encoding, enabling high-quality rendered images with ~50% memory savings.
(1/n) 🧵
tl;dr: Novel framework that integrates 3D awareness into VAE latent space using correspondence-aware encoding, enabling high-quality rendered images with ~50% memory savings.
(1/n) 🧵
"EdgeRunner" (#ICLR2025) from #Nvidia & PKU introduces an auto-regressive auto-encoder for mesh generation, supporting up to 4000 faces at 512³ resolution. 🤩
Their mesh tokenization algorithm (adapted from EdgeBreaker) achieves ~50% compression (4-5 tokens per face vs 9), making training efficient.
Their mesh tokenization algorithm (adapted from EdgeBreaker) achieves ~50% compression (4-5 tokens per face vs 9), making training efficient.
February 13, 2025 at 10:34 PM
"EdgeRunner" (#ICLR2025) from #Nvidia & PKU introduces an auto-regressive auto-encoder for mesh generation, supporting up to 4000 faces at 512³ resolution. 🤩
Their mesh tokenization algorithm (adapted from EdgeBreaker) achieves ~50% compression (4-5 tokens per face vs 9), making training efficient.
Their mesh tokenization algorithm (adapted from EdgeBreaker) achieves ~50% compression (4-5 tokens per face vs 9), making training efficient.