arXiv subjects

Binyan Chen

Publications and source records attributed to Binyan Chen.

3 recordsLinked to original sources

arXiv2026

DiaHeads: Jointly Generating Coordinated 3D Dialogue Heads with Non-verbal Cues from Dual Audio

Audio-driven 3D head animation has made considerable progress in generation realism, yet generating coordinated behavior for two-person dialogue remains difficult, because current dialogue head generation methods overlook the modeling of non-verbal behavior semantics between the two participants. To address this limitation, we present DiaHeads, a framework that generates the behavior of both participants jointly. The two audio streams are concatenated and decoded together, so each participant's motion is conditioned on the other, which makes the interaction part of the generation itself. We further introduce four discrete non-verbal cues, gaze-on, head shaking, nodding, and smiling, and fuse them with the continuous parameters in a shared latent space through a product-of-experts posterior. These cues carry the interaction semantics and constrain how the continuous motion is generated. A flow-based prior over the shared latent variable replaces the standard Gaussian prior and makes the latent motion temporally dependent. These designs therefore yield coordinated 3D dialogue heads. To support this task we build CANDOR-FG, a dialogue corpus derived from CANDOR and augmented with synchronized FLAME parameters and 3D gaze for both interlocutors. Experiments on CANDOR-FG show that DiaHeads achieves competitive per-participant fidelity and substantial gains in interpersonal coordination over single-speaker and dyadic baselines. In addition, ablations confirm that joint generation, the flow-based prior, and multi-cue supervision are the sources of this coordination. Finally, a user study shows that the advantage of DiaHeads comes from perceived interactional coordination rather than from raw motion fidelity alone.

cs.MM↗

arXiv2026

Self-Verifier Capture in Unified Visual Generators: Diagnosing and Repairing Closed-Loop Self-Reward

Image-space reward requires decoding and a separate judge; unified visual generators can instead reuse their understanding pathway for decode-free, self-supplied reward. Optimizing against this verifier closes the self-reward loop; shared trainable weights let one update move generation and scoring even with the reward gradient stopped. We call the resulting failure self-verifier capture: self-reward rises while independently measured quality falls. Matched semantic/denoising by shared/frozen cells on Scale-RAE isolate two routes: the policy exploits on-manifold blind spots of a frozen semantic judge, while shared updates additionally rewrite the judge. A frozen judge also becomes stale, and repeated self-refresh yields progressively weaker recoveries. The diagnosis yields a two-part repair: locking the warmed reward read blocks rewriting, while sparse, parameter-disjoint external anchors counter residual exploitation. Unanchored rollouts retain semantic reward across multiple latent noise levels, providing dense latent reward that terminal image-space judging cannot supply, while a drift-triggered controller allocates anchors to high-drift rollouts and propagates their calibration within each group. At matched budgets, the controller improves on fixed-dose anchoring, increasingly so as coverage tightens. Both the unanchored failure and the repair transfer to the higher-performing, architecturally distinct Show-o2. Across both bases, the complete method significantly outperforms a construct-matched every-rollout judge on GenEval and DPG-Bench while externally scoring only 1/16 of rollouts. At 1/32 coverage, it scores higher in all four comparisons and remains jointly non-inferior.

cs.CV↗

arXiv2026

Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation

Identity-preserving text-to-video generation aims to synthesize a video that accurately follows a textual description while maintaining the recognizability of a user-specified subject throughout. The IPVG26 challenge extends this framework from a single holistic prompt to a temporally structured specification. The model additionally receives a sequence of timestamped action captions and must render the subject performing these actions in the specified order. This temporal structure presents a challenge not encountered in previous identity-preserving generation tasks, as the subject must continuously perform a scripted sequence of distinct actions while maintaining a consistent identity. However, end-to-end video generators are prone to appearance drift as motion accumulates and the depicted actions change. We address this challenge with a training-free, three-stage pipeline framework. An action-aware prompt polishment stage first rewrites the inputs into image-generation prompts that specify the terminal state of each action. An identity-preserving generation stage then produces the keyframe sequence by conditioning each frame jointly on the reference identity and its predecessor, thereby decoupling time-invariant appearance from time-varying pose. Finally, an identity-aware inference enhancement stage synthesizes the intermediate segments using multi-reference guidance and identity-driven noise searching, both of which reinforce identity fidelity during sampling. Our method ranked third on the official Track 2 leaderboard, demonstrating competitive performance and strong generality.

cs.CV↗