Audio-driven talking head synthesis has achieved significant progress, yet existing methods face critical trade-offs among generation quality, inference efficiency, and cross-ethnic generalization. Diffusion-based approaches produce high-fidelity results but suffer from slow inference due to iterative denoising, while GAN-based methods achieve faster speed at the cost of reduced motion naturalness and limited generalization. To address these challenges, we propose FlowTalk, a novel framework that enables real-time high-fidelity talking head video synthesis. Our approach leverages Flow Matching technology to perform efficient motion modeling in a decoupled motion space rather than pixel space, achieving significant speedup while maintaining generation quality. Specifically, we adopt an off-the-shelf motion extractor to disentangle facial appearance from motion, and employ an OT-based flow matching model with a transformer architecture to predict identity-agnostic motion sequences conditioned on audio features. To improve cross-ethnic generalization, we train on a balanced combination of DH-FaceVid1K and HDTF datasets with HuBert-CN as the audio encoder. Experimental results demonstrate that FlowTalk achieves over 100 FPS in motion-space inference with 32 ODE solver steps, approximately 5 times faster than diffusion-based baselines with 500 steps, while preserving comparable visual quality in lip synchronization, facial expressions, and head movements. This efficiency, further enhanced through TensorRT deployment, enables truly real-time generation. Our framework provides an effective and practical solution for real-time talking head generation applications.
Talking-head video generation has seen significant progress with the rise of multimodal and multiethnic datasets; however, existing systems still suffer from stability issues when dealing with occluded or partially missing facial regions in the input. In this work, we present our model developed for the ACM Multimedia Asia 2025 Grand Challenge on Multimodal Multiethnic Talking-Head Video Generation. Built upon the Hallo2 framework, our model introduces two key improvements to enhance inference robustness and usability. First, it detects cases where facial regions are heavily occluded or incompletely detected and automatically substitutes a static image across all frames, preventing inference failures and maintaining visual consistency when facial information is insufficient. Second, it supports multi-input inference, enabling the generation of multiple talking-head videos within a single execution process. These modifications result in a more reliable and flexible talking-head generation pipeline, suitable for diverse multimodal datasets and large-scale evaluation.
Existing talking-head video generation methods using speech audio often suffer from high computational costs or degraded identity preservation due to reliance on external super-resolution models. To address these issues, we propose a lightweight framework that efficiently synthesizes high-quality videos by leveraging audio-aware implicit 3D features and a generator trained on large-scale data. Experimental results demonstrate that our method achieves comparable or superior visual quality and identity preservation, while ensuring high inference efficiency. This balance of computational performance and visual quality suggests broad applicability in real-world scenarios such as social media and online education.
For designers in the manufacturing industry, discovering design opportunities from large-scale User-Generated Content (UGC) is crucial in physical product conceptual design, yet manual analysis is inefficient. This problem limits product designers' ability and hinders the manufacturing industry's ability to produce products that do not easily meet users' needs. This study proposes Co-Designer, a human-AI collaboration framework that helps designers efficiently discover opportunities from UGC. To ensure the development of software that designers need, this study proposed a formative study with eight professional designers, who identified workflow pain points and requirements for AI support. Based on these insights, Co-Designer applies Aspect-Based Sentiment Analysis (ABSA) to transform UGC into a structured "aspect-sentiment" knowledge base, enabling an AI design partner that adapts to designer profiles and dynamically acts as an "analyst" or "creative teammate." Following the Double Diamond design model, it can invoke workflows to translate insights into design strategies. To evaluate the software's system availability, this study used a mixed-methods approach with 10 designers, complemented by expert evaluation, which showed that Co-Designer significantly improves efficiency, insight, creativity, and planning. This work introduces a new paradigm positioning AI as a strategic collaborator and creativity catalyst in physical product concept design.
Traditional video compression continues to advance, but the gains in efficiency are diminishing and come at the cost of higher computational complexity. Despite achieving competitive rate-distortion results, current neural video codecs (NVCs) generally lack support for a wide range of quality levels, often requiring multiple models to achieve flexible rate control, which increases both training cost and deployment complexity. To address the limitations of both traditional codecs and current NVCs, we propose a hybrid video compression framework that integrates traditional codecs with hint-guided video frame interpolation (VFI), a learning-based technique for synthesizing intermediate frames. By using decoded reference frames and leveraging compressed-domain hints to guide interpolation, our method improves both motion compensation and reconstruction quality. This design combines the efficiency of traditional codecs with the adaptability of neural interpolation, achieving consistent rate-distortion performance and supporting a wide range of quality levels on standard benchmarks.
