All papers

The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency.

Introduces ELMA, a benchmark dataset for generating long-term human motions from multi-segment instructions with anaphoric references.

Anchors feed-forward visual geometry to LiDAR-inertial motion to produce metrically accurate trajectories and 3D Gaussian reconstructions.

Presents a previsualization workflow combining physical video capture and generative reshooting to evaluate and manage visual hallucination risk.

Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques.

Introduces an annotated dataset of handwritten student answer sheets with complex formulas, text, tables, and noise artifacts.

This paper introduces a multi-sensor, multi-temporal satellite imagery dataset designed for pretraining Earth observation foundation models.

The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback.

The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites.

Introduces a benchmark dataset and baseline architecture for reconstructing standalone 3D garments from single images.

Introduces a reinforcement learning objective that groups prompt tokens into semantic spans to refine text-to-image prompt rewriting.

The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks.

The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation.

The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision.

The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry.

Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories.

The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities.

Proposes a framework to dress 3D meshes with aligned materials by decoupling visible surface estimation from observation-guided 3D completion.

Repurposes a pretrained video generator into a single-pass feed-forward system to estimate 4D motion, depth, and camera poses.

The paper proposes a training method combining data refinement with reinforcement learning and distillation to improve long-horizon multimodal search agents.

This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding.

The paper introduces a 4D latent cache to reuse static and dynamic visual representations for efficient conditioning in video generation models.

The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors.

The paper introduces a skill-decoupled on-policy distillation method to merge vision-language experts without conflicting gradients or reward hacking.

Introduces an adaptive patch-wise denoising framework that stops easy patches early to speed up autoregressive video diffusion.

Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models.

Accelerates video diffusion models by using closed-loop calibrated caching instead of fixed heuristic thresholds across denoising steps.

Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs.

Proposes a memory-efficient cross-attention method for distilling large paired audio-visual datasets into compact synthetic sets.

Aligns robot policy representations with retrieved pretraining examples to maintain generalization during low-data fine-tuning.

Adapts latent video diffusion models with target conditioning and semantic-geometric alignment for coherent generative frame inbetweening.

Guides vision-language navigation training using progress attention and spatial structure losses to improve action grounding and generalization.

Integrates execution progress awareness into vision-language navigation to suppress completed instruction segments and improve multi-step trajectory matching.

Presents a training-free framework for prompt-driven audio-video editing that uses flow differences to balance cross-modal changes and preservation.

Proposes a hierarchical model merging method to combine modality-specific trackers into a unified RGB-X tracking framework.

Proposes Temporal-Response Guidance to direct latent distillation supervision for video-language models using local temporal perturbation responses.

Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters.

Presents a training-free framework combining variance-adaptive particle search with Metropolis-Hastings refinement for budget-constrained audio-visual alignment.

Introduces a shift-aware identity-guided latent refinement framework using dual tokens to resolve target drift in referring audio-visual segmentation.

Generates seamlessly looping 3D cinemagraphs from static 3D Gaussian splats using periodic deformation fields guided by video models.

The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

Proposes a reinforcement learning framework with verifiable rewards and error grounding to align multimodal emotion descriptions with human preferences.

The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass.

This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions.

This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features.

The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar.

Presents ReDiS, a self-evolution framework separating exploration and candidate diagnosis to improve multimodal reasoning in remote sensing.

Distills multiple speech attributes from four teacher models into a single shared speech encoder.

The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech.

The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities.

The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation.

Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units.

Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation.

The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals.

The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens.

The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation.

Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale.