How can one reconstruct the complete 3D interior of a wood block using only photos of its surfaces? 🪵
At SIGGRAPH'25 (Thursday!), Maria Larsson will present *Mokume*: a dataset of 190 diverse wood samples and a pipeline that solves this inverse texturing challenge. 🧵👇

Methods like NeRF and Gaussian Splats model the world as radioactive fog, rendered using alpha blending. This produces great results.. but are volumes the only way to get there?🤔 Our new SIGGRAPH'25 paper directly reconstructs surfaces without heuristics or regularizers.

Dr.Jit+Mitsuba just added support for fused neural networks, hash grids, and function freezing to eliminate tracing overheads. This significantly accelerates optimization &realtime workloads and enables custom Instant NGP and neural material/radiosity/path guiding projects. What will you do with it?

Rendering nerds! Check out our latest work "Vector-Valued Monte Carlo Integration Using Ratio Control Variates" that has just gotten the best paper award at SIGGRAPH 2025. This paper presents a method that reduces variance of a wide range of rendering and diff. rendering tasks with negligible cost.

Our #SGP25 work studies a simple and effective way to uniformly sample implicit surfaces by casting rays. (1/9)

“Uniform Sampling of Surfaces by Casting Rays” w/ @abhishekmadan.bsky.social @nmwsharp.bsky.social and Alec Jacobson

The latest development version of Dr.Jit now provides built-in support for evaluating and training MLPs (including fusing them into rendering workloads). They compile to efficient Tensor Core operations via NVIDIA's Cooperative Vector extension. Details: drjit.readthedocs.io/en/latest/nn...

Inverse rendering has become a standard tool for 3D reconstruction problems. However, recovering high-frequency appearance textures is challenging. In our SIGGRAPH 2025 paper, we propose several techniques to robustly reconstruct complex appearances (e.g., human skin). 1/n

Fun new paper at #SIGGRAPH2025:

What if instead of two 6-sided dice, you could roll a single "funky-shaped" die that gives the same statistics (e.g, 7 is twice as likely as 4 or 10).

Or make fair dice in any shape—e.g., dragons rather than cubes?

That's exactly what we do! 1/n

cseweb.ucsd.edu/~tzli/Score_...
I wrote down some random notes about the connection between score matching (aka diffusion models) and Stein's unbiased risk estimate (SURE). It shows why optimal denoising lead to optimal score. Not a new observation but doesn't seem to be talked enough in literature.

We are excited to present a SIGGRAPH Asia paper exploring a new application of inverse rendering to Tomographic Volumetric Additive Manufacturing (TVAM), a new light-based 3D printing technology that can print objects in less than a minute.

Following over 1.5 years of hard work (w/@njroussel.bsky.social &@rtabbara.bsky.social), we just released a brand-new version of Dr.Jit (v1.0), my lab's differentiable rendering compiler along with an updated Mitsuba (v3.6). The list of changes is insanely long—here is what we're most excited about🧵

Hi! I'll share classical and less known graphics (and other) papers here. The first paper I'll share is "Model-Based 3D Hand Pose Estimation from Monocular Video" from de la Gorce et al. in 2011. They proposed one of the first differentiable rasterizers that can properly handle edge discontinuities.