Pre-trained Video Models

• Pre-trained video models are high-capacity neural architectures that learn spatiotemporal representations by capturing both appearance and motion dynamics.
• They employ diverse pre-training strategies such as contrastive learning, masked modeling, and generative denoising over massive video corpora.
• These models incorporate inductive biases for temporal consistency and dynamic prediction, enabling effective zero-shot, few-shot, and multimodal generalization.

Pre-trained video models are high-capacity neural architectures—typically based on transformers or diffusion networks—trained on large-scale video corpora to learn spatiotemporal representations that encode both appearance and motion dynamics. These models operate as general-purpose visual backbones and can be repurposed for diverse downstream tasks such as recognition, understanding, generative simulation, and foundation modeling. Recent advances leverage architectures including video transformers, latent diffusion networks, and multimodal mixtures of frozen expert encoders, with pre-training objectives spanning masked modeling, contrastive learning, and generative denoising losses. Pre-trained video models exhibit inductive biases for temporal consistency, semantic alignment, and dynamic prediction, underpinning progress in fields such as robotic skill acquisition, world simulation, open-vocabulary recognition, semantic segmentation, and video restoration.

1. Architectures and Pre-training Strategies

Transformer-based Models

VideoPrism employs a factorized space–time Vision Transformer (ViT) backbone, pretrained via a two-stage protocol: (1) video–text contrastive alignment injects global semantic priors, followed by (2) masked video modeling with global-local distillation and token shuffling to focus representation learning on the video modality (Zhao et al., 20 Feb 2024). SimVLT (All-in-One Transformer) fuses video and text streams in a unified ViT stack, employing a parameter-free token rolling operation to model temporal dependencies without flattening, significantly reducing complexity while maintaining state-of-the-art performance across video-language tasks (Wang et al., 2022).

Diffusion and Generative Models

Latent video diffusion models (e.g., Stable Video Diffusion, Wan2.1-T2V) operate over sequences of encoded latent frames, modeling the forward noising process and learning to reconstruct clean sequences using denoising neural networks with spatiotemporal attention layers. Pre-training is conducted on internet-scale video corpora using the mean-squared error between sampled noise and predicted residuals (Wang et al., 27 May 2024, Chen et al., 26 Sep 2025, Chen et al., 10 Dec 2024). Universal action-conditioned modules and motion-reinforced losses further enable these backbones to simulate complex dynamics and action-conditioned transitions (He et al., 10 Feb 2025).

Multimodal and Mixture-of-Experts

VPT (Video Pre-trained Transformer) integrates frozen vision, audio, ASR, and scene-graph experts to encode video clips, combining their embeddings through non-linear projection before ingesting them into a LLM backbone. This “embedding → backbone → prediction head” schema facilitates flexible multimodal fusion and has been used for video question answering, retrieval, and captioning tasks (Day et al., 2023).

2. Pre-training Data and Scale

Large-scale curated datasets are fundamental for effective video model pre-training. HVM-1 models exploit nearly 5,000 hours of egocentric and continuous human-like video, sampled from datasets such as Ego4D, AVA, Epic-Kitchens, and SAYCam, emphasizing sustained temporal regularity over short action clips (Orhan, 25 Jul 2024). VideoPrism trains on a heterogenous corpus with 36 million high-quality video-caption pairs and 582 million video clips with noisy parallel text, leveraging diverse temporal and semantic distributions (Zhao et al., 20 Feb 2024). Generative video diffusion models are typically pretrained on billions of web videos, encoding complex motion patterns and rich commonsense physics (He et al., 10 Feb 2025).

3. Pre-trained Models as Foundation Backbones

Pre-trained video models serve as adaptable foundation models for vision, supporting zero-shot transfer, few-shot adaptation, and parameter-efficient fine-tuning. LoRA modules are frequently used to inject low-rank adaptation into transformer or diffusion blocks, updating only small subsets of parameters (Acuaviva et al., 28 Oct 2025, Chen et al., 26 Sep 2025). These models have demonstrated superior sample efficiency and cross-modal generalization compared to language-only pre-training, achieving strong performance in domains such as ARC-style reasoning, visual games, route planning, cellular automata prediction, and cross-source semantic segmentation (Acuaviva et al., 28 Oct 2025, Chen et al., 26 Sep 2025).

Unified Task Representation

UniVid introduces the concept of “visual sentences”—sequential context pairs of input–output modalities—enabling the same pretrained video diffusion transformer to handle both image and video tasks (generation, segmentation, depth estimation) without architectural changes (Chen et al., 26 Sep 2025).

4. Zero-Shot and Few-Shot Learning

Pre-trained video backbones facilitate zero-shot transfer by encoding high-level semantic and temporal priors. VideoPrism, using only lightweight heads atop its frozen backbone, achieves state-of-the-art results on 31 out of 33 benchmarks, including action recognition, localization, captioning, video understanding for science, and question answering (Zhao et al., 20 Feb 2024). The VD-IT framework fully exploits the temporal-semantic coherence present in generatively pretrained text-to-video diffusion models, outperforming discriminatively pretrained video transformers in referring video object segmentation (Zhu et al., 18 Mar 2024).

5. Applications Across Vision and Robotics

Skill Learning and Simulation

No-data Imitation Learning (NIL) leverages pre-trained video diffusion models as frozen “expert” generators, creating synthetic demonstration videos for 3D policy learning in diverse morphologies, such as humanoids and quadrupeds, replacing real motion-capture data collection (Albaba et al., 13 Mar 2025). Dynamic World Simulation (DWS) transforms static pre-trained generative backbones into interactive, action-controllable world simulators for robotics and reinforcement learning via lightweight action-conditioning modules and motion-reinforced losses (He et al., 10 Feb 2025).

Compression and Restoration

Extreme video compression methods use frozen, pre-trained video diffusion models to reconstruct skipped frames, outperforming standard codecs in perceptual quality at ultra-low bitrates (0.02–0.07 bpp) by shifting generative burden to the decoder (Li et al., 14 Feb 2024). For restoration, bilevel temporal-consistency priors—semantic clustering in seed space and progressive optical flow warping—enable zero-shot video enhancement and artifact removal far surpassing classical and supervised baselines (Wang et al., 19 Mar 2025).

Semantic Segmentation and Matting

Zero-shot video semantic segmentation leverages the temporal modules of pre-trained diffusion backbones to produce scene-consistent segmentation maps, rivaling or exceeding supervised approaches on benchmarks such as VSPW, Cityscapes, and CamVid (Wang et al., 27 May 2024). OmnimatteZero applies off-the-shelf video diffusion networks and novel attention-guidance to achieve layer decomposition, object removal, and background inpainting in real time, without any training or per-video optimization (Samuel et al., 23 Mar 2025).

6. Multimodal, Open-Vocabulary, and Temporal Perception

MOV extends open-vocabulary video classification by incorporating optical flow and audio streams via frozen CLIP encoders, combined with cross-modal transformer fusion heads. This enables high accuracy on both seen and novel event classes, outperforming prior VLMs on datasets such as Kinetics-700, VGGSound, UCF101, and HMDB51 (Qian et al., 2022).

Temporal Perceiving Video-Language Pre-training (TemPVL) introduces fine-grained temporal alignment through moment retrieval, text localization, and large-scale merging strategies, substantially improving both zero-shot and fully supervised performance on retrieval, QA, captioning, and action localization tasks (Ma et al., 2023).

7. Inductive Biases, Limitations, and Future Directions

Pre-trained video models encode strong inductive biases for spatial composition, temporal consistency, and dynamic prediction, supporting rapid few-shot adaptation and robust generalization even on tasks outside their original modalities (Acuaviva et al., 28 Oct 2025). Nevertheless, computational demands for inference and fine-tuning remain high, and modeling of long-range dependencies and multimodal integration (audio, text) require further architectural innovation (Zhao et al., 20 Feb 2024, Orhan, 25 Jul 2024). Emerging directions include modular adapter compositions, scaling studies, efficient sampling, hierarchical representations, and mechanistic interpretability, with anticipated impact extending to embodied intelligence, physical simulation, and general-purpose foundation modeling.

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References

• "VideoPrism: A Foundational Visual Encoder for Video Understanding" (Zhao et al., 20 Feb 2024)
• "HVM-1: Large-scale video models pretrained with nearly 5000 hours of human-like video data" (Orhan, 25 Jul 2024)
• "Pre-Trained Video Generative Models as World Simulators" (He et al., 10 Feb 2025)
• "NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models" (Albaba et al., 13 Mar 2025)
• "Extreme Video Compression with Pre-trained Diffusion Models" (Li et al., 14 Feb 2024)
• "Temporal-Consistent Video Restoration with Pre-trained Diffusion Models" (Wang et al., 19 Mar 2025)
• "Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models" (Wang et al., 27 May 2024)
• "OmnimatteZero: Fast Training-free Omnimatte with Pre-trained Video Diffusion Models" (Samuel et al., 23 Mar 2025)
• "UniVid: Unifying Vision Tasks with Pre-trained Video Generation Models" (Chen et al., 26 Sep 2025)
• "Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation" (Zhu et al., 18 Mar 2024)
• "DistInit: Learning Video Representations Without a Single Labeled Video" (Girdhar et al., 2019)
• "Video Pre-trained Transformer: A Multimodal Mixture of Pre-trained Experts" (Day et al., 2023)
• "All in One: Exploring Unified Video-Language Pre-training" (Wang et al., 2022)
• "Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and LLMs" (Qian et al., 2022)
• "Temporal Perceiving Video-Language Pre-training" (Ma et al., 2023)
• "Rethinking Visual Intelligence: Insights from Video Pretraining" (Acuaviva et al., 28 Oct 2025)
• "Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation" (Chen et al., 10 Dec 2024)