2 citations · 3 across the 5 of their papers we have counts for
5 papers
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
Wonkwang Lee, Jongwon Jeong, Taehong Moon +4
Motion synthesis for diverse object categories holds great potential for 3D content creation but remains underexplored due to two key challenges: (1) the lack of comprehensive moti…
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens
Jaehyeon Kim, Taehong Moon, Keon Lee +1
We introduce ResGen, an efficient Residual Vector Quantization (RVQ)-based generative model for high-fidelity generation with fast sampling. RVQ improves data fidelity by increasin…
Practical and Reproducible Symbolic Music Generation by Large Language Models with Structural Embeddings
Seungyeon Rhyu, Kichang Yang, Sungjun Cho +3
Music generation introduces challenging complexities to large language models. Symbolic structures of music often include vertical harmonization as well as horizontal counterpoint,…
DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors
Keon Lee, Dong Won Kim, Jaehyeon Kim +2
Large-scale latent diffusion models (LDMs) excel in content generation across various modalities, but their reliance on phonemes and durations in text-to-speech (TTS) limits scalab…
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
Jaehyeon Kim, Keon Lee, Seungjun Chung +1
With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach…