BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension

• URL: http://arxiv.org/abs/2606.08674v2
• Date: Tue, 09 Jun 2026 14:14:17 GMT
• Title: BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension
• Abstract summary: BioVid is a data-driven autoregressive video generation framework.<n>It learns the temporal structure of biological behaviors directly from training data.<n>It achieves a Wasserstein-1 distance of 1.24 against the ground truth.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing video generation frameworks treat sequence duration as an externally prescribed parameter -- fixed frame counts or text prompts -- producing clips whose temporal boundaries are decoupled from the statistical structure of real behavioral data. This assumption is fundamentally misaligned with biological behavior, where action duration varies naturally across individuals and instances and is encoded in the data itself. We present BioVid, a data-driven autoregressive video generation framework that learns the temporal structure of biological behaviors directly from training data, including their natural length distributions. In the first stage, a Finite Scalar Quantization GAN (FSQ-R3GAN) tokenizer encodes each video frame into a compact discrete representation, combining the stabilized relativistic training objective of R3GAN with FSQ's guaranteed codebook utilization to achieve high-fidelity spatial reconstruction without codebook collapse. In the second stage, a causal Transformer models the resulting token sequences autoregressively and learns to emit an End-of-Sequence (EOS) token when the behavioral event reaches semantic closure, with the termination distribution emerging naturally from the training data rather than any human-specified constraint. Experiments on a human drinking behavior dataset (NTU RGB+D, A001, n=94) demonstrate that BioVid's generated length distribution closely matches that of held-out test data, achieving a Wasserstein-1 distance of 1.24 against the ground truth -- compared to 6.05 for a fixed-length baseline and 15.48 for VideoGPT -- while maintaining competitive spatial fidelity.

Related papers

• Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detection [10.264738258468254]
  Cross-scale coupling mismatch is a new forensic signal.<n>In natural videos, macro-level temporal dynamics and micro-level residual patterns are intrinsically coupled by the unified imaging physics pipeline.<n>But AI generators, whose training objectives do not explicitly preserve this joint distribution, systematically violate this coupling.<n>Detecting such mismatch is challenging because it requires independently extracting information at both scales.
  arXiv  Detail & Related papers  (2026-09-01T05:23:48Z)
• Self Gradient Forcing: Native Long Video Extrapolation [60.00513917489133]
  Self Gradient Forcing restores historical context-gradient gap.<n>Code and models will be released to advance research on autoregressive video generation.
  arXiv  Detail & Related papers  (2026-07-22T16:59:23Z)
• A non-invasive video-based method for individual identification of wildlife using gait dynamics [21.468912995256698]
  This paper presents a fully automated video-based pipeline for wildlife gait analysis and individual identification.<n>The proposed pipeline uses Segment Anything Model 3 (SAM3) to generate high-quality RGB and binary silhouette masks.<n>Cosine is then used to compare gait signatures, enabling similarity-based clustering of individuals without reliance on physical or invasive markings.
  arXiv  Detail & Related papers  (2026-07-05T21:47:21Z)
• Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions [60.2784641643737]
  Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models.<n>We study the structural identifiability of continuous-time physical laws from raw pixels.<n>We prove that a level-set slope-coverage condition ensures the learned latent space is locally affine to the true physical state.
  arXiv  Detail & Related papers  (2026-05-27T13:22:29Z)
• TIE: Time Interval Encoding for Video Generation over Events [50.66585165263848]
  Director-style prompting, robotic action prediction, and interactive video agents demand temporal grounding over concurrent events.<n>Modern video generators represent time as discrete points through point-wise positional encodings.<n>Time Interval TIE is a principled, plug-and-play interval-aware generalization.
  arXiv  Detail & Related papers  (2026-05-11T13:23:14Z)
• From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation [54.68405211129937]
  Exo-Seqto-Ego video generation aims to synthesize a first-person view from a synchronized third-person and corresponding camera poses.<n> synchronized exo-ego data inherently introduces substantial-temporal discontinuities, violating the smooth-motion assumptions of standard video generation benchmarks.<n>We propose Syn2Ex-Forcing, a sequential sequence model that interpolates between the source and target videos to form a single continuous signal.
  arXiv  Detail & Related papers  (2026-04-15T12:32:25Z)
• Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals [0.0]
  We formalize a taxonomy of four behavioral fraud patterns (P1-P4) covering inter-event timing, burst structure, multi-account graph motifs, and velocity-rule trigger rates.<n>We benchmark CTGAN, TVAE, GaussianCopula, and TabularARGN on IEEE-CIS Fraud Detection and the Amazon Fraud dataset.<n>The P1-P4 framework extends to any domain with entity-level sequential data, including healthcare and network security.
  arXiv  Detail & Related papers  (2026-04-13T19:36:00Z)
• From Frames to Sequences: Temporally Consistent Human-Centric Dense Prediction [22.291273919939957]
  We develop a scalable synthetic data pipeline that generates human frames and motion-aligned sequences with pixel-accurate depth, normals, and masks.<n>We train a unified ViT-based dense predictor that injects an explicit geometric human prior via CSE embeddings.<n>Our two-stage training strategy, combining static pretraining with dynamic sequence supervision, enables the model first to acquire robust spatial representations and then refine temporal consistency across motion-aligned sequences.
  arXiv  Detail & Related papers  (2026-02-02T05:28:58Z)
• Stable Video Infinity: Infinite-Length Video Generation with Error Recycling [76.91310169118408]
  We propose Stable Video Infinity (SVI) that is able to generate infinite-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines.<n> SVI incorporates Error-Recycling Fine-Tuning, a new type of efficient training that recycles the Diffusion Transformer's self-generated errors into supervisory prompts.<n>We evaluate SVI on three benchmarks, including consistent, creative, and conditional settings, thoroughly verifying its versatility and state-of-the-art role.
  arXiv  Detail & Related papers  (2025-10-10T09:45:46Z)
• Transform-Equivariant Consistency Learning for Temporal Sentence Grounding [66.10949751429781]
  We introduce a novel Equivariant Consistency Regulation Learning framework to learn more discriminative representations for each video. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted. In particular, we devise a self-supervised consistency loss module to enhance the completeness and smoothness of the augmented video.
  arXiv  Detail & Related papers  (2023-05-06T19:29:28Z)
• A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild [72.0226493284814]
  We propose a unified framework for event-based frame that performs deblurring ad-hoc.<n>Our network consistently outperforms previous state-of-the-art methods on frame, single image deblurring, and the joint task of both.
  arXiv  Detail & Related papers  (2023-01-12T18:19:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.