most citedAny-to-Any Generation via Composable Diffusion

29 citations · 51 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20232 cited

Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization

Tanmay Gautam, Reid Pryzant, Ziyi Yang +2

Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in…

cs.CV202314 cited

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Ziyi Yang, Xinyu Gao, Wen Zhou +3

Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering. Nonetheless, cutting-edge dynamic neural rendering methods rely h…

cs.RO20233 cited

Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents

Ziyi Yang, Shreyas S. Raman, Ankit Shah +1

Recent advancements in large language models (LLMs) have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and ge…

cs.CL20232 cited

i-Code Studio: A Configurable and Composable Framework for Integrative AI

Yuwei Fang, Mahmoud Khademi, Chenguang Zhu +8

Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Int…

cs.CL20231 cited

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data

Ziyi Yang, Mahmoud Khademi, Yichong Xu +16

The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…

cs.CV202329 cited

Any-to-Any Generation via Composable Diffusion

Zineng Tang, Ziyi Yang, Chenguang Zhu +2

We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any comb…