We propose new methods for combining NDFs in microfacet theory, enabling a wider range of surface statistics. The new BSDFs that follow allow for independent adjustment of appearance at grazing angles, and can’t be represented by linear blends of single-NDF BSDFs. We derive importance sampling for a symmetric operator that blends NDFs uniformly, and introduce a new asymmetric operator that supports NDF variation with elevation. We also extend Smith’s model to support piecewise-constant NDF and material variations with

Modern data-driven image generation models often surpass traditional graphics techniques in quality. However, while traditional modeling and animation tools allow precise control over the image generation process in terms of interpretable quantities, e.g., shapes and reflectances, endowing learned models with such controls is generally difficult.

CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads.

We propose embodied scene-aware human pose estimation where we estimate 3D poses based on a simulated agent's proprioception and scene awareness, along with external third-person observations. Unlike prior methods that often resort to multistage optimization, non-causal inference, and complex contact modeling to estimate human pose and human scene interactions, our method is one stage, causal, and recovers global 3D human poses in a simulated environment.

Motion capture (mocap) data has been the most popular data source for computer animation techniques that combine deep reinforcement learning and motion imitation to produce lifelike motions and perform diverse skills. However, mocap data for specialized skills can be costly to acquire at scale while there exists an enormous corpus of athletic motion data in the form of video recordings.

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in guided diffusion modeling to achieve test-time controllability of trajectories, which is normally only associated with rule-based systems. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups while accounting for the surrounding environment context.

This article presents a clock-forwarded, inverter-based short-reach simultaneous bi-directional (ISR-SBD) physical layer (PHY) targeted for die-to-die communication over silicon interposers or similar high-density interconnect. Short-reach links of this type are increasingly important to support larger systems built with chiplets and multiple die and to facilitate the shift to medium- and long-range optical communication based on silicon photonics. This project explores the advantages of simultaneous bi-directional signaling (SBD) over other bandwidth-doubling techniques (e.g., PAM4).

We propose a novel, real-time algorithm for recoloring images to improve the experience for a color vision deficient observer.
The output is temporally stable and preserves luminance, the most important visual cue. It runs in 0.2 ms per frame on a GPU.

The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namely conformal keypoint detection and geometric uncertainty propagation, into the two-stage paradigm and propose the first pose estimator that endows an estimation with provable and computable worst-case error bounds.

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields (NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as pre-trained models and extra depth signals, and by non-trivial patch-based rendering. In this paper, we present Frequency regularized NeRF (FreeNeRF), a surprisingly simple baseline that outperforms previous methods with minimal modifications to the plain NeRF. We analyze the key challenges in few-shot neural rendering and find that frequency plays an important role in NeRF’s training.