arXiv subjects

Unsang Park

Publications and source records attributed to Unsang Park.

11 recordsLinked to original sources

arXiv2026

Fréchet Oral Distance: A Coordinate-Free Metric and Benchmark for Speech-Driven Oral Animation

Recently, speech-driven facial and oral animation technologies have been used in various fields such as education, speech therapy, and healthcare. But methods to objectively evaluate the quality of generated results are still limited. Accordingly, we propose the Fréchet Oral Distance (FOD), which uses embeddings from an Oral Motion Encoder (OME) and a TIMIT-Vis reference distribution. In experiments, FOD reproduced model rankings similar to the coordinate-based metric, Lip Vertex Error (LVE), and showed trends consistent with user preference. Our framework provides objective and reproducible benchmarking without relying on expensive equipment or subjective evaluation, and it can be extended to compare a variety of 2D/3D models.

cs.CV↗

arXiv2026

CASE: Complementary Articulation-Aware Speech Encoder to Enhance Speech-Driven 3D Facial Animation

General-purpose self-supervised speech encoders provide strong acoustic representations, but their pretraining objectives are not explicitly aligned with the visible articulatory dynamics required by speech-driven 3D facial animation. We propose CASE (Complementary Articulation-aware Speech Encoder), a complementary speech encoder adapted by contrastively aligning short speech clips with continuous 3D lip-mesh VQ-VAE latents. After adaptation, the geometry branch is removed, and the audio-only CASE representation is fused with the baseline SSL speech representation through residual cross-attention, while retaining the motion-decoder architecture and objective. Across four VOCASET baselines, CASE reduces mean lip vertex error by 2.8-6.0%, and mean full-face vertex error decreases in every system. Under the same fusion protocol, CASE yields lower mean LVE than the unadapted AV-SE stream on all four generators, with model-dependent margins. Because adaptation uses Korean speech-geometry pairs and evaluation uses English VOCASET, the result is consistent with cross-language downstream transfer under a shared English-pretrained audio-visual prior.

cs.CV↗

arXiv2026

Local Evidence, Global Failure: Diagnosing Speech-Vision Temporal Composition in Audio-Visual Language Models

Audio-visual large language models (AV-LLMs) can fail at full-video speech-vision decisions even when useful visual evidence is locally recoverable. We introduce Temporal Evidence Decomposition (TED), a training-free diagnostic framework that separates local visual verification from speech-relative aggregation. Automatic speech recognition supplies a transcript and utterance interval. TED queries audio-free windows with the transcript for Temporal Alignment and Forecasting, or answer candidates for Cross-Modal Temporal Binding, then aggregates evidence relative to that interval. Across four frozen AV-LLMs on SVHalluc, Temporal Forecasting accuracy rises from 29.3-33.7% under full-video questioning to 79.3-82.2% with TED. Query replacement reduces recovery; the observed anchor remains beneficial when temporal-region availability is held fixed. Physical-isolation and timing-shortcut controls test alternative explanations. The findings distinguish locally recoverable evidence from successful full-video decisions, motivating explicit separation of narration content, utterance timing, and time-indexed visual evidence.

cs.CV↗

arXiv2026

KoUniTalk: A Lightweight Articulation-Centered Korean-English 3D Talking Face Benchmark

High-quality 3D talking face datasets remain largely English-centric, and Korean 3D facial motion data are difficult to combine with standard English benchmarks because of differences in mesh topology, spatial scale, coordinate system, and temporal sampling. We present KoUniTalk, a lightweight articulation-centered Korean-English 3D talking face benchmark that retargets VOCASET and the released Korean speech-based 3D talking face data to a shared mesh topology using deformation transfer. Rather than proposing a new deformation-transfer algorithm or a full-head identity-preserving avatar dataset, KoUniTalk provides an identity-neutral canonical output space for controlled speech-driven facial articulation training and evaluation across English and Korean. The unified template contains 1,176 vertices and focuses on the mouth and adjacent lower- and mid-face regions, reducing the output dimensionality from 15,069 and 72,147 dimensions to 3,528 dimensions, corresponding to 4.27-fold and 20.45-fold reductions compared with VOCASET/FLAME and the original Korean mesh, respectively. To examine whether retargeting preserves speech-relevant motion, we evaluate semantic mouth-landmark trajectories, including mouth opening, mouth width, aperture ratio, and mouth-opening dynamics. Since the official test set of the Korean dataset is not publicly released, we additionally define a subject-disjoint Korean benchmark split. The processed matched benchmark contains 22 speakers, 4,978 sequences, and 642,781 frames, enabling Korean-English cross-dataset evaluation of speech-driven 3D facial animation models in a single compact articulation-template space. Source-reported inventory counts are listed separately from these processed counts.

cs.CV↗

arXiv2026

Simulate, record, verify: A language-portable framework for muscle-grounded articulatory QA (extended version)

Articulatory data describe what the tongue looks like, but geometry alone does not explain the muscle-level why behind a configuration or how to move it toward a target posture. We present a simulator-based framework that constructs this supervision from controlled biomechanical inputs. Each simulated configuration is stored with its generating muscle state and geometric properties in a structured fact record. Gold answers are derived before linguistic realization, and every naturalized output is checked against its source record, so the same records support new languages and question types without re-simulation. We instantiate the framework as 3DTongueQA using the ArtiSynth Badin tongue model. From 295,115 meshes, we construct 891,156 record-checked QA instances per language, and the checker detects over 96.9% of injected corruptions. Korean and Spanish realizations and a new question type are added from the same records, confirming portability. A SpiralNet++-Qwen3 probe reaches 62.9 Muscle EM, above 44.0 for nearest-neighbor transfer and 7.2 for a zero-shot commercial LLM given the same mesh, while mesh shuffling drops it to near zero. The supervision is learnable, useful beyond retrieval, and grounded in paired geometry. It reflects simulator-defined states rather than measured physiology. Code, templates, and QA sets are available at https://github.com/esh0504/muscle-grounded-qa.

cs.CV↗

arXiv2026

SNF-Bench: Separating Static Drift from Natural Flow in Long-Horizon Fixed-Camera Video Generation

Long-horizon video generation is evaluated with whole-frame metrics that reward motion and temporal consistency. For fixed-camera nature scenes this creates an ambiguity: motion of water, fire, smoke, or rain is desirable, whereas motion of the background is an error. A system can therefore score well on motion while its scene drifts, or on consistency while its flow stagnates. We introduce SNF-Bench, an evaluation framework for long-horizon fixed-camera generation that partitions each scene into static support and dynamic flow and reports static fidelity, flow persistence with absolute magnitude, and drift leakage separately, never as one score. Drift leakage is interpretive context rather than a headline measurement. Each factor is validated mechanistically rather than by correlation with preference: we inject global translation, rotation, and scale drift and progressive late freezing at known severity into real generations, and require each factor to respond in its stated direction and to remain selective against corruptions it does not target. Auditing publicly released long-horizon text-conditioned checkpoints under one recorded common inference configuration, plus an image-conditioned track with released-pipeline references and a deployment-sensitivity panel, we find that whole-frame motion and static-region drift induce near-opposite orderings of the same outputs. At maximum controlled translation, fBD and NBF rise to $1.86\times$ and $1.32\times$ baseline, but whole-frame Dynamic Degree reaches only $1.07\times$---rewarding the corruption. SNF-Bench measures where motion occurs and whether it persists; it does not measure physical realism. Project page: https://minar09.github.io/snfbench/.

cs.CV↗

arXiv2026

Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion

Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability-motion trade-off in fixed-camera long-horizon nature video generation, where the two failure modes can be more clearly separated than in moving-camera settings. We propose Steady-Forcing, a memory and training framework combining a persistent visual anchor (V-Sink), an exponential moving-average motion memory (EMA-Sink), block-relative temporal encoding, periodic cache purification, and distillation from a Wan2.1-14B teacher with motion-rewarded priors under task-focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi-minute autoregressive rollouts. Evaluations across seven baselines show that Steady-Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under-penalize fixed-camera artifacts as well as rewarding drift-induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation - motivating future task-specific benchmarks for static-camera nature-flow evaluation. Project page: https://minar09.github.io/steadyforcing/

cs.CV↗

arXiv2026

Attention Frequency Modulation: Training-Free Spectral Modulation of Diffusion Cross-Attention

Cross-attention is the primary interface through which text conditions latent diffusion models, yet its step-wise multi-resolution dynamics remain under-characterized, limiting principled training-free control. We cast diffusion cross-attention as a spatiotemporal signal on the latent grid by summarizing token-softmax weights into token-agnostic concentration maps and tracking their radially binned Fourier power over denoising. Across prompts and seeds, encoder cross-attention exhibits a consistent coarse-to-fine spectral progression, yielding a stable time-frequency fingerprint of token competition. Building on this structure, we introduce Attention Frequency Modulation (AFM), a plug-and-play inference-time intervention that edits token-wise pre-softmax cross-attention logits in the Fourier domain: low- and high-frequency bands are reweighted with a progress-aligned schedule and can be adaptively gated by token-allocation entropy, before the token softmax. AFM provides a continuous handle to bias the spatial scale of token-competition patterns without retraining, prompt editing, or parameter updates. Experiments on Stable Diffusion show that AFM reliably redistributes attention spectra and produces substantial visual edits while largely preserving semantic alignment. Finally, we find that entropy mainly acts as an adaptive gain on the same frequency-based edit rather than an independent control axis.

cs.CV↗

arXiv2025

WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design

Accurate spatial prediction of power integrity issues, such as IR drop, is critical for reliable VLSI design. However, traditional simulation-based solvers are computationally expensive and difficult to scale. We address this challenge by reformulating IR drop estimation as a pixel-wise regression task on heterogeneous multi-channel physical maps derived from circuit layouts. Prior learning-based methods treat all input layers (e.g., metal, via, and current maps) equally, ignoring their varying importance to prediction accuracy. To tackle this, we propose a novel Weakness-Aware Channel Attention (WACA) mechanism, which recursively enhances weak feature channels while suppressing over-dominant ones through a two-stage gating strategy. Integrated into a ConvNeXtV2-based attention U-Net, our approach enables adaptive and balanced feature representation. On the public ICCAD-2023 benchmark, our method outperforms the ICCAD-2023 contest winner by reducing mean absolute error by 61.1% and improving F1-score by 71.0%. These results demonstrate that channel-wise heterogeneity is a key inductive bias in physical layout analysis for VLSI.

cs.LG↗

arXiv2024

Facial Expression-Enhanced TTS: Combining Face Representation and Emotion Intensity for Adaptive Speech

We propose FEIM-TTS, an innovative zero-shot text-to-speech (TTS) model that synthesizes emotionally expressive speech, aligned with facial images and modulated by emotion intensity. Leveraging deep learning, FEIM-TTS transcends traditional TTS systems by interpreting facial cues and adjusting to emotional nuances without dependence on labeled datasets. To address sparse audio-visual-emotional data, the model is trained using LRS3, CREMA-D, and MELD datasets, demonstrating its adaptability. FEIM-TTS's unique capability to produce high-quality, speaker-agnostic speech makes it suitable for creating adaptable voices for virtual characters. Moreover, FEIM-TTS significantly enhances accessibility for individuals with visual impairments or those who have trouble seeing. By integrating emotional nuances into TTS, our model enables dynamic and engaging auditory experiences for webcomics, allowing visually impaired users to enjoy these narratives more fully. Comprehensive evaluation evidences its proficiency in modulating emotion and intensity, advancing emotional speech synthesis and accessibility. Samples are available at: https://feim-tts.github.io/.

cs.SD↗

arXiv2024

Swish-T : Enhancing Swish Activation with Tanh Bias for Improved Neural Network Performance

We propose the Swish-T family, an enhancement of the existing non-monotonic activation function Swish. Swish-T is defined by adding a Tanh bias to the original Swish function. This modification creates a family of Swish-T variants, each designed to excel in different tasks, showcasing specific advantages depending on the application context. The Tanh bias allows for broader acceptance of negative values during initial training stages, offering a smoother non-monotonic curve than the original Swish. We ultimately propose the Swish-T$_{\textbf{C}}$ function, while Swish-T and Swish-T$_{\textbf{B}}$, byproducts of Swish-T$_{\textbf{C}}$, also demonstrate satisfactory performance. Furthermore, our ablation study shows that using Swish-T$_{\textbf{C}}$ as a non-parametric function can still achieve high performance. The superiority of the Swish-T family has been empirically demonstrated across various models and benchmark datasets, including MNIST, Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. The code is publicly available at https://github.com/ictseoyoungmin/Swish-T-pytorch.

cs.LG↗