Video Generation Foundation Models

Updated 16 October 2025

Video generation foundation models are large-scale neural architectures that synthesize and comprehend dynamic video content using extensive spatiotemporal pretraining and multimodal alignment.
They integrate advanced techniques like masked video modeling, diffusion frameworks, and cross-modal conditioning to achieve high-fidelity video synthesis, editing, and control.
Robust evaluation metrics and efficient training practices ensure these models excel in tasks such as text-to-video, image-to-video, and narrative-driven video generation.

Video generation foundation models are large-scale, general-purpose neural architectures designed to synthesize or understand video content by leveraging representational pretraining on vast and diverse spatiotemporal datasets. These models extend the foundation paradigm from static images to dynamic sequences, integrating both generative and discriminative principles to support a wide array of video understanding, synthesis, editing, retrieval, and control tasks. They typically combine innovations in data curation, spatiotemporal encoding, multimodal alignment (text–video, image–video, audio–video), and scalable optimization to deliver robust, high-fidelity, and temporally coherent outputs across diverse downstream applications.

1. Model Architectures and Pretraining Paradigms

Video generation foundation models exhibit architectural diversity, but share a common reliance on hierarchical, spatiotemporal tokenization and large neural backbones—transformers or diffusion models operating in the latent space compressed by video-specific Variational Autoencoders (VAEs). Key techniques and variations include:

Masked Video Modeling (MVM) and Generative Pretraining: Models such as InternVideo (Wang et al., 2022) employ high-masking strategies (e.g., masking ~90% of 3D video patches) within a ViT-based encoder-decoder that reconstructs missing content, enabling robust learning of fine-grained spatiotemporal structure.
Contrastive and Multimodal Learning: Contrastive Video-Language objectives align spatiotemporal representations with associated text, leveraging backbone text encoders (often from the CLIP family) with additional cross-attention and captioning heads to integrate semantics (Wang et al., 2022, Madan et al., 6 May 2024).
Diffusion and Flow-based Models: Most generative video models employ diffusion-based frameworks, where latent video tokens are generated via iterative denoising in a compressed space (Chen et al., 2023, Polyak et al., 17 Oct 2024, Chen et al., 7 Feb 2025, Wan et al., 26 Mar 2025, Seawead et al., 11 Apr 2025, Gao et al., 10 Jun 2025, Zhang et al., 21 Aug 2025). Rectified flow transformers, as in Goku (Chen et al., 7 Feb 2025), replace diffusion with a continuous, velocity-based transformation xₜ = t·x₁ + (1–t)·x₀, trained to minimize the velocity prediction error.

Model	Backbone	Latent Compression	Pretraining Approach
InternVideo	3D ViT + cross-modal	3D patch (~90% masking)	MVM + contrastive video-language
VideoCrafter1	3D U-Net	Per-frame (VAE)	Diffusion (T2V & I2V) + FPS control
Movie Gen	Transformer (LLaMa3)	8× spatiotemporal (TAE)	Joint T2I/T2V flow-matching
Wan	DiT (Diffusion Trans)	3D causal VAE (4×8×8)	Flow-matching + cross-modal
Seaweed-7B	Hybrid Dual-Stream	Causal VAE (e.g., 48×)	Joint T2I/T2V, staged curriculum
Step-Video-T2V	DiT, 3D FullAttn	Video-VAE (16×16×8)	Joint T2I/T2V + DPO
Waver	Hybrid-Stream DiT	Latent VAE	Flow-matching + representation align
Goku	Full-attn Transformer	Joint VAE (img/video)	Rectified flow + joint training

Such architectures are frequently paired with scalable and efficient training algorithms (e.g., parallelism, checkpointing, memory-efficient attention variants) to accommodate extremely long tokens sequences for high-resolution, long-duration video generation (Polyak et al., 17 Oct 2024, Chen et al., 7 Feb 2025, Gao et al., 10 Jun 2025).

2. Multimodal and Conditional Generation

Foundation video models are unified frameworks supporting text-to-video (T2V), image-to-video (I2V), and, in some cases, text-to-image (T2I) generation:

Conditional Token Fusion: VideoCrafter1 (Chen et al., 2023), Waver (Zhang et al., 21 Aug 2025), and FullDiT (Ju et al., 25 Mar 2025) integrate conditioning signals—text, images, depth, pose, camera, audio—through dedicated modality-embedding branches with cross-attention or full self-attention fusion, resulting in models that support single and composite conditional synthesis.
Unified Input/Output Representations: The hybrid stream architectures in Waver (Zhang et al., 21 Aug 2025) and Seaweed-7B (Seawead et al., 11 Apr 2025) enable flexible switching between modality-specific and shared parameter processing for robust multi-task learning.
Control Mechanisms: The field has shifted from purely text prompts to richer control signals (e.g., pose, depth, viewpoint) (Ma et al., 22 Jul 2025). Adapter-based approaches (e.g., ControlNet) are being replaced by architectures (FullDiT) that natively tokenizes multiple control streams for joint conditioning without parameter overhead or branch conflicts.
Training-Free and Bridging Approaches: BIVDiff (Shi et al., 2023) demonstrates that bridging framewise image diffusion outputs (e.g., ControlNet) with video diffusion models for temporal smoothing can enable general-purpose, training-free video synthesis and editing.

These developments have enabled models to address advanced tasks such as video personalization, instruction-guided editing, text-driven compositional control, and video-to-audio joint generation (Polyak et al., 17 Oct 2024, Wan et al., 26 Mar 2025).

3. Evaluation Metrics, Benchmarks, and Capabilities

Performance evaluation for video generation foundation models relies on both objective metrics and human preference studies:

Key Metrics:
- Fréchet Video Distance (FVD), Inception Score (IS), and CLIP Similarity (CLIPSim) assess distributional similarity, diversity, and semantic alignment.
- Custom benchmarks such as VBench, DPG-Bench, Step-Video-T2V-Eval, and the Artificial Analysis Arena evaluate aspects ranging from text alignment and motion quality to aesthetic appeal.
- Specialized benchmarks like NarrLV (Feng et al., 15 Jul 2025) introduce narrative-centric evaluation for long video synthesis using the “Temporal Narrative Atom” (TNA) concept, with MLLM-based QA for coverage and coherence.
Reported Results: SOTA models such as Goku achieve VBench scores of 84.85; Seaweed-7B matches or exceeds larger models in win rates and prompt following on image-to-video, despite only 7B parameters (Seawead et al., 11 Apr 2025). Waver and Step-Video-T2V reach top-3 in leaderboard rankings for both T2V and I2V.
Tradeoffs: Video duration, spatial/temporal fidelity, and conditional control represent key axes of benchmarking. The ability to generate high-resolution (720p/1080p), multi-second, temporally coherent, and visually diverse sequences is considered a benchmark of foundation model effectiveness.

4. Efficiency, Scaling, and Training Practices

The scaling properties and resource efficiency of foundation video models are central to their accessibility and adoption:

Compression Strategies: Deep video-VAEs (e.g., 8× spatial × 4/8× temporal) serve to limit token count and memory footprint, enabling efficient transformer/diffusion processing even for long videos (Polyak et al., 17 Oct 2024, Ma et al., 14 Feb 2025). Compression ratio formulas, such as r = C/(3×dₜ×dₕ×d_w), quantify VAE efficiency (Seawead et al., 11 Apr 2025, Gao et al., 10 Jun 2025).
Parallelism/Optimization: Advanced activation checkpointing, sequence, data, and model parallelism, and specialized fused kernels are now standard to scale training to extremely large models (e.g., Movie Gen Video: 30B parameters, context >70k tokens; Goku: 2B–8B+ parameters).
Cost-Effective Design: Mid-sized models like Seaweed-7B can be trained from scratch (<700k H100 GPU hours) to reach performance close to or exceeding much larger models. Multi-stage curricula (e.g., image-only → joint image-video → fine-tuning) and runtime resource balancing further optimize compute efficiency (Seawead et al., 11 Apr 2025).
Distillation and Fast Sampling: Techniques such as multi-stage distillation (Seedance 1.0 (Gao et al., 10 Jun 2025)) and low NFE (Neural Function Evaluations) inference pipelines allow generative models to operate up to 62× faster at inference with minimal quality loss.

5. Applications, Capabilities, and Generalization

Video generation foundation models are deployed in an expanding array of domains, demonstrating generality and adaptability:

Creative Content Synthesis: High-fidelity, cinematic video clips for film, advertising, and social media (Polyak et al., 17 Oct 2024, Zhang et al., 21 Aug 2025).
Editing and Personalization: Instruction-driven video editing, text-based compositional changes, and user-image-specific video personalization (Polyak et al., 17 Oct 2024, Wan et al., 26 Mar 2025).
Medical and Scientific Use: Domain-adapted generative models (e.g., Endora (Li et al., 17 Mar 2024)) simulate endoscopy procedures for education, annotation, and 3D scene consistency.
Physics-Consistent Generation: Recent frameworks (e.g., VideoREPA (Zhang et al., 29 May 2025)) inject physics knowledge into diffusion generators by aligning token-level relationships to video understanding foundation models, leading to more physically plausible sequences.
Long-Narrative Generation: Foundation models now underpin long video systems, yet are shown by NarrLV (Feng et al., 15 Jul 2025) to be bottlenecked in narrative coherence and evolution as the number of “Temporal Narrative Atoms” in a prompt increases.

Capability	Model(s)/Paper(s)	Evaluation/Metric
Text-to-video (T2V)	Movie Gen, Step-Video-T2V	VBench, Step-Video-T2V-Eval, human prefer.
Image-to-video (I2V)	VideoCrafter1, Waver, Wan	Artificial Analysis Arena, CLIPSim, FVD
Instructional editing	Movie Gen, Wan	Human eval., prompt following
Physics consistency	VideoREPA	VideoPhy, context-specific PC scores
Controllable multi-modality	FullDiT, Controllable Gen Surv	FullBench, user-defined conditional coverage

Generalization is further demonstrated by cross-modal and in-context capabilities, as in RealGeneral (Lin et al., 13 Mar 2025), which uses video models as unified visual priors for general image and conditional frame prediction tasks.

6. Challenges, Limitations, and Future Directions

Despite significant advances, several key challenges remain:

Long-term Coherence and Narrative Expression: As shown by NarrLV (Feng et al., 15 Jul 2025), current models, even with strong short-range dynamics, struggle with narrative unit coherence when extended to complex, multi-TNA prompts, suggesting a need for explicit temporal logic or autoregressive designs.
Physics and Causality: Diffusion and flow-based models remain limited in capturing complex object interactions or strict physical constraints. Finetuning with physics-aligned losses (e.g., TRD in VideoREPA) improves but does not close this gap.
Scaling and Accessibility: While models such as Wan (1.3B) and Seaweed-7B show that medium-sized architectures suffice for many tasks, further innovation in resource-constrained training, data curation, and model compression is essential for democratizing access.
Rich Conditioning and Control: The taxonomy outlined in (Ma et al., 22 Jul 2025) highlights ongoing work in universal control—integrating more modalities (audio, camera, motion, depth) transparently and controllably into generative pipelines.
Evaluation Methodologies: Current benchmarks capture aspects of visual quality, temporal coherence, and text alignment, but comprehensive metrics for physical realism, multi-shot/staged compositionality, and generalization to out-of-domain tasks are still under development.

Suggested directions include (a) explicit causal modeling and autoregressive transformers for extended temporal horizons (Madan et al., 6 May 2024, Feng et al., 15 Jul 2025); (b) advanced multimodal fusion and cross-modal alignment strategies; (c) continual/lifelong learning paradigms for adaptation to evolving data streams; (d) improved physics-injected objectives; and (e) community-driven open sourcing of models and metrics (Wan et al., 26 Mar 2025, Zhang et al., 21 Aug 2025).

7. Impact, Openness, and Community Resources

The movement toward openness and reproducibility is prominent in the current generation of video foundation models:

Open Source Commitments: Major models—Wan (Wan et al., 26 Mar 2025), Seaweed-7B (Seawead et al., 11 Apr 2025), FullDiT (Ju et al., 25 Mar 2025), Waver (Zhang et al., 21 Aug 2025), Step-Video-T2V (Ma et al., 14 Feb 2025)—release code, weights, evaluation benchmarks, and curated prompt datasets, supporting rigorous comparison and further research.
Benchmark Availability: Publicly accessible leaderboards (Artificial Analysis Arena, Step-Video-T2V-Eval, NarrLV) and curated model repositories (e.g., Awesome-Controllable-Video-Generation (Ma et al., 22 Jul 2025), ViFM Survey (Madan et al., 6 May 2024)) facilitate independent assessment and inspire reproducible research.
Model Accessibility: Efficient, consumer-grade architectures (Wan 1.3B: 8.19GB VRAM (Wan et al., 26 Mar 2025)), fast inference pipelines (Seaweed-7B APT: 1-step, high FPS (Seawead et al., 11 Apr 2025), Seedance 1.0: 10× acceleration (Gao et al., 10 Jun 2025)) and broad toolkit support lower the entry barrier for practical deployment in production and experimentation.

These trends foster accelerated progress in both creative industries and academic research, laying the foundation for the next leap in video synthesis, editing, and understanding.

In summary, video generation foundation models synthesize and understand complex spatiotemporal data by combining scalable architectures, rigorous pretraining objectives, and rich cross-modal alignment mechanisms. Innovations in efficiency, control, and evaluation, alongside open dissemination of models and benchmarks, continue to shape a landscape that is rapidly pushing the frontiers of what is achievable in visual content generation.