6 papers
Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
Chen Si, Qianyi Wu, Chaitanya Amballa +1
Realistic sound simulation plays a critical role in many applications. A key element in sound simulation is the room impulse response (RIR), which characterizes how sound propagate…
Discrete Langevin-Inspired Posterior Sampling
Chaitanya Amballa, Sattwik Basu, Jorge VanÄo Sampedro +1
We study posterior sampling for inverse problems in discrete state spaces using discrete diffusion models as generative priors. While continuous diffusion models have become widely…
Contrastive Diffusion Guidance for Spatial Inverse Problems
Sattwik Basu, Chaitanya Amballa, Zhongweiyang Xu +3
We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable. Although generative inverse solvers hav…
Can NeRFs See without Cameras?
Chaitanya Amballa, Sattwik Basu, Yu-Lin Wei +3
Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how op…
AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking
Xilin Jiang, Qiaolin Wang, Junkai Wu +30
Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…
Learning Energy-based Variational Latent Prior for VAEs
Debottam Dutta, Chaitanya Amballa, Zhongweiyang Xu +2
Variational Auto-Encoders (VAEs) are known to generate blurry and inconsistent samples. One reason for this is the "prior hole" problem. A prior hole refers to regions that have hi…