arXiv subjects

Thanh Hoang-Minh

Publications and source records attributed to Thanh Hoang-Minh.

4 recordsLinked to original sources

arXiv2026

Towards Reliable Human Evaluations in Gesture Generation: Insights from a Community-Driven State-of-the-Art Benchmark

We review human evaluation practices in automatic, speech-driven 3D gesture generation and find a lack of standardisation and frequent use of flawed experimental setups. This leads to a situation where it is impossible to know how different methods compare, or what the state of the art is. In order to address common shortcomings of evaluation design, and to standardise future user studies in gesture-generation works, we introduce a detailed human evaluation protocol for the widely-used BEAT2 motion-capture dataset. Using this protocol, we conduct large-scale crowdsourced evaluation to rank six recent gesture-generation models -- each trained by its original authors -- across two key evaluation dimensions: motion realism and speech-gesture alignment. Our results show that 1) motion realism has become a saturated evaluation measure on the BEAT2 dataset, with older models performing on par with more recent approaches; 2) previous findings of high speech-gesture alignment do not hold up under rigorous evaluation, even for specialised models; and 3) the field must adopt disentangled assessments of motion quality and multimodal alignment for accurate benchmarking in order to make progress. To drive standardisation and enable new evaluation research, we release five hours of synthetic motion from the benchmarked models; over 750 rendered video stimuli from the user studies -- enabling new evaluations without requiring model reimplementation -- alongside our open-source rendering script, and 16,000 pairwise human preference votes collected for our benchmark.

cs.CV↗

arXiv2026

A conversational gesture synthesis system based on emotions and semantics

Along with the explosion of large language models, improvements in speech synthesis, advancements in hardware, and the evolution of computer graphics, the current bottleneck in creating digital humans lies in generating character movements that correspond naturally to text or speech inputs. In this work, we present DeepGesture, a diffusion-based gesture synthesis framework for generating expressive co-speech gestures conditioned on multimodal signals - text, speech, emotion, and seed motion. Built upon the DiffuseStyleGesture model, DeepGesture introduces novel architectural enhancements that improve semantic alignment and emotional expressiveness in generated gestures. Specifically, we integrate fast text transcriptions as semantic conditioning and implement emotion-guided classifier-free diffusion to support controllable gesture generation across affective states. To visualize results, we implement a full rendering pipeline in Unity based on BVH output from the model. Evaluation on the ZeroEGGS dataset shows that DeepGesture produces gestures with improved human-likeness and contextual appropriateness. Our system supports interpolation between emotional states and demonstrates generalization to out-of-distribution speech, including synthetic voices - marking a step forward toward fully multimodal, emotionally aware digital humans.

cs.HC↗

arXiv2025

Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs

Knowledge graphs offer a structured representation of real-world entities and their relationships, enabling a wide range of applications from information retrieval to automated reasoning. In this paper, we conduct a systematic comparison between traditional rule-based approaches and modern deep learning methods for link prediction. We focus on KBGAT, a graph neural network model that leverages multi-head attention to jointly encode both entity and relation features within local neighborhood structures. To advance this line of research, we introduce \textbf{GCAT} (Graph Collaborative Attention Network), a refined model that enhances context aggregation and interaction between heterogeneous nodes. Experimental results on four widely-used benchmark datasets demonstrate that GCAT not only consistently outperforms rule-based methods but also achieves competitive or superior performance compared to existing neural embedding models. Our findings highlight the advantages of attention-based architectures in capturing complex relational patterns for knowledge graph completion tasks.

cs.LG↗

arXiv2024

Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis

Current evaluation practices in speech-driven gesture generation lack standardisation and focus on aspects that are easy to measure over aspects that actually matter. This leads to a situation where it is impossible to know what is the state of the art, or to know which method works better for which purpose when comparing two publications. In this position paper, we review and give details on issues with existing gesture-generation evaluation, and present a novel proposal for remedying them. Specifically, we announce an upcoming living leaderboard to benchmark progress in conversational motion synthesis. Unlike earlier gesture-generation challenges, the leaderboard will be updated with large-scale user studies of new gesture-generation systems multiple times per year, and systems on the leaderboard can be submitted to any publication venue that their authors prefer. By evolving the leaderboard evaluation data and tasks over time, the effort can keep driving progress towards the most important end goals identified by the community. We actively seek community involvement across the entire evaluation pipeline: from data and tasks for the evaluation, via tooling, to the systems evaluated. In other words, our proposal will not only make it easier for researchers to perform good evaluations, but their collective input and contributions will also help drive the future of gesture-generation research.

cs.HC↗