16 citations · 40 across the 87 of their papers we have counts for
38 papers · 1 filter
PAVAS: Physics-Aware Video-to-Audio Synthesis
Oh Hyun-Bin, Yuhta Takida, Toshimitsu Uesaka +2
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing vis…
Locality-Aware Continual Unlearning for Diffusion Models
Naveen George, Naoki Murata, Yuhta Takida +2
Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time. Existing unlearning met…
MeanFlow Transformers with Representation Autoencoders
Zheyuan Hu, Chieh-Hsin Lai, Ge Wu +2
MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used…
Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi +2
Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampling process remains relatively u…
MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval
Qiyu Wu, Shuyang Cui, Satoshi Hayakawa +3
Multimodal retrieval, which seeks to retrieve relevant content across modalities such as text or image, supports applications from AI search to contents production. Despite the suc…
Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity
Naoki Yoshida, Satoshi Hayakawa, Yuhta Takida +3
In this study, we propose an enhancement to the similarity computation mechanism in multimodal contrastive pretraining frameworks such as CLIP. Prior theoretical research has demon…
SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator
Yuhta Takida, Satoshi Hayakawa, Takashi Shibuya +6
Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adv…
The Principles of Diffusion Models
Chieh-Hsin Lai, Yang Song, Dongjun Kim +2
This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematic…
Reverse Engineering of Music Mixing Graphs with Differentiable Processors and Iterative Pruning
Sungho Lee, Marco Martínez-Ramírez, Wei-Hsiang Liao +4
Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. We extend the prior works to reflect the compositional…
CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models
Zheyuan Hu, Chieh-Hsin Lai, Yuki Mitsufuji +1
Flow map models such as Consistency Models (CM) and Mean Flow (MF) enable few-step generation by learning the long jump of the ODE solution of diffusion models, yet training remain…
TITAN-Guide: Taming Inference-Time AligNment for Guided Text-to-Video Diffusion Models
Christian Simon, Masato Ishii, Akio Hayakawa +4
In the recent development of conditional diffusion models still require heavy supervised fine-tuning for performing control on a category of tasks. Training-free conditioning via g…
Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification
Kazuki Shimada, Archontis Politis, Iran R. Roman +10
This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the c…
Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation
Anshuk Uppal, Yuhta Takida, Chieh-Hsin Lai +1
Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The -VAE framework introduces a hyperparameter t…
Fx-Encoder++: Extracting Instrument-Wise Audio Effects Representations from Mixtures
Yen-Tung Yeh, Junghyun Koo, Marco A. Martínez-Ramírez +3
General-purpose audio representations have proven effective across diverse music information retrieval applications, yet their utility in intelligent music production remains limit…
ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors
Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao +3
Music mastering style transfer aims to model and apply the mastering characteristics of a reference track to a target track, simulating the professional mastering process. However,…
Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impact
Kevin Rojas, Ye He, Chieh-Hsin Lai +3
Classifier-Free Guidance (CFG) is a widely used technique for conditional generation and improving sample quality in continuous diffusion models, and its extensions to discrete dif…
Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Yonghyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +7
While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identi…
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Junyoung Seo, Jisang Han, Jaewoo Jung +9
We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challen…
Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity
Yuya Kobayashi, Yuhta Takida, Takashi Shibuya +1
Recently, Generative Adversarial Networks (GANs) have been successfully scaled to billion-scale large text-to-image datasets. However, training such models entails a high training…
Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance
Akio Hayakawa, Masato Ishii, Takashi Shibuya +1
We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis. Inspired by traditional…
Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk +7
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specifi…
A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?
Yigitcan Özer, Woosung Choi, Joan Serrà +3
We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic compari…
Dyadic Mamba: Long-term Dyadic Human Motion Synthesis
Julian Tanke, Takashi Shibuya, Kengo Uchida +2
Generating realistic dyadic human motion from text descriptions presents significant challenges, particularly for extended interactions that exceed typical training sequence length…
Can Large Language Models Predict Audio Effects Parameters from Natural Language?
Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez +3
In music production, manipulating audio effects (Fx) parameters through natural language has the potential to reduce technical barriers for non-experts. We present LLM2Fx, a framew…