Anand Bhattad
@anandbhattad.bsky.social
Incoming Assistant Professor at Johns Hopkins University | RAP at Toyota Technological Institute at Chicago | web: https://anandbhattad.github.io/ | Knowledge in Generative Image Models, Intrinsic Images, Image-based Relighting, Inverse Graphics
[8/10] This simple idea surprisingly scales to a wide range of scenes: from clean setups like a cat on a table or a stack of bowls... to messy, real-world scenes (yes, even Alyosha’s office).
March 29, 2025 at 7:36 PM
[8/10] This simple idea surprisingly scales to a wide range of scenes: from clean setups like a cat on a table or a stack of bowls... to messy, real-world scenes (yes, even Alyosha’s office).
Can we create realistic renderings of urban scenes from a single video while enabling controllable editing: relighting, object compositing, and nighttime simulation?
Check out our #3DV2025 UrbanIR paper, led by @chih-hao.bsky.social that does exactly this.
🔗: urbaninverserendering.github.io
Check out our #3DV2025 UrbanIR paper, led by @chih-hao.bsky.social that does exactly this.
🔗: urbaninverserendering.github.io
March 16, 2025 at 3:39 AM
Can we create realistic renderings of urban scenes from a single video while enabling controllable editing: relighting, object compositing, and nighttime simulation?
Check out our #3DV2025 UrbanIR paper, led by @chih-hao.bsky.social that does exactly this.
🔗: urbaninverserendering.github.io
Check out our #3DV2025 UrbanIR paper, led by @chih-hao.bsky.social that does exactly this.
🔗: urbaninverserendering.github.io
[1/3] From a single image to the 3D world—it’s possible with the right data, & we have it for you🚀
We’re thrilled to release our 360° video dataset. Training a simple conditional diffusion model with explicit camera control can synthesize novel 3D scenes—all from a single image input! #NeurIPS2024
We’re thrilled to release our 360° video dataset. Training a simple conditional diffusion model with explicit camera control can synthesize novel 3D scenes—all from a single image input! #NeurIPS2024
December 11, 2024 at 9:32 PM
[1/3] From a single image to the 3D world—it’s possible with the right data, & we have it for you🚀
We’re thrilled to release our 360° video dataset. Training a simple conditional diffusion model with explicit camera control can synthesize novel 3D scenes—all from a single image input! #NeurIPS2024
We’re thrilled to release our 360° video dataset. Training a simple conditional diffusion model with explicit camera control can synthesize novel 3D scenes—all from a single image input! #NeurIPS2024
Here are a few more random relighting! ✨
How accurate are these results? That's very hard to tell at the moment 🤔
But our tests on the MIT data, our user study, plus our qualitative results all point to us being on the right track. Gen models seem to know about how light interacts with our world.
How accurate are these results? That's very hard to tell at the moment 🤔
But our tests on the MIT data, our user study, plus our qualitative results all point to us being on the right track. Gen models seem to know about how light interacts with our world.
December 5, 2024 at 3:58 PM
Here are a few more random relighting! ✨
How accurate are these results? That's very hard to tell at the moment 🤔
But our tests on the MIT data, our user study, plus our qualitative results all point to us being on the right track. Gen models seem to know about how light interacts with our world.
How accurate are these results? That's very hard to tell at the moment 🤔
But our tests on the MIT data, our user study, plus our qualitative results all point to us being on the right track. Gen models seem to know about how light interacts with our world.
Quantitative results 📊
Evaluation is done on challenging MIT multi-illum dataset (only real data we have with GT and similar lighting conditions across scenes). By combining latent intrinsic with generative models, we cut down the error (RMSE) by 35%! See our paper for qualitative results.
Evaluation is done on challenging MIT multi-illum dataset (only real data we have with GT and similar lighting conditions across scenes). By combining latent intrinsic with generative models, we cut down the error (RMSE) by 35%! See our paper for qualitative results.
December 5, 2024 at 3:58 PM
Quantitative results 📊
Evaluation is done on challenging MIT multi-illum dataset (only real data we have with GT and similar lighting conditions across scenes). By combining latent intrinsic with generative models, we cut down the error (RMSE) by 35%! See our paper for qualitative results.
Evaluation is done on challenging MIT multi-illum dataset (only real data we have with GT and similar lighting conditions across scenes). By combining latent intrinsic with generative models, we cut down the error (RMSE) by 35%! See our paper for qualitative results.
LumiNet's architecture is quite simple! 🏗️
Given two images (source image to be relit and target lighting condition), we first extract latent intrinsic image representations using our NeurIPS2024 work for both these images and then train a simple latent ControlNet.
Given two images (source image to be relit and target lighting condition), we first extract latent intrinsic image representations using our NeurIPS2024 work for both these images and then train a simple latent ControlNet.
December 5, 2024 at 3:58 PM
LumiNet's architecture is quite simple! 🏗️
Given two images (source image to be relit and target lighting condition), we first extract latent intrinsic image representations using our NeurIPS2024 work for both these images and then train a simple latent ControlNet.
Given two images (source image to be relit and target lighting condition), we first extract latent intrinsic image representations using our NeurIPS2024 work for both these images and then train a simple latent ControlNet.