VideoJAM Framework for Video Synthesis

• VideoJAM Framework is a generative video synthesis architecture that fuses appearance and motion, delivering coherent temporal dynamics and refined visual details.
• It employs dual prediction techniques and an inner-guidance mechanism to optimize both motion consistency and image fidelity during the denoising process.
• Its minimal adjustments to existing models enable broad applicability in text-to-video generation and content editing, streamlining rapid domain adaptation.

The VideoJAM Framework is a generative video modeling architecture that augments conventional video diffusion models with a joint appearance-motion representation, aiming to address deficiencies in the temporal coherence of synthetic video sequences. By conditioning both training and inference on combined appearance and motion signals, VideoJAM advances the fidelity and physical plausibility of generated motion while preserving or enhancing visual details. The framework is designed for broad applicability, requiring minimal architectural modification and no changes to data pipelines or model scaling procedures.

1. Joint Appearance-Motion Representation

At the core of VideoJAM is the explicit fusion of appearance and motion information within both the model input and output projections. During training, the model input receives a concatenation of the noised video latent ($x_t$) and the noised motion latent ($d_t$), where $d_t$ is typically derived from an optical flow representation:

$[x_t, d_t] \in \mathbb{R}^{2C_{TAE}p^2}$

Here, $C_{TAE}$ is the latent channel dimension and $p \times p$ is the patch size used for spatial unrolling. The output head is similarly split: one branch predicts the denoised appearance ($u^x$), and the other predicts the denoised motion ($u^d$). The full model output can be notated as:

$$u^+([x_t, d_t], y, t; \theta') = M([x_t, d_t]W_{in}^+, y, t; \theta) \, W_{out}^+$$

where $M$ denotes the sequence of attention blocks, $y$ is the text or conditional input, and $W_{in}^+$, $W_{out}^+$ are the learned linear projections. This dual-pathway structure compels the model to develop a unified latent representation capturing both photometric and kinematic characteristics, addressing the observed trade-off under traditional pixel-based losses that favor static image fidelity at the expense of realistic motion (Chefer et al., 4 Feb 2025).

2. Training Objective and Dual Prediction Strategy

The training regime for VideoJAM extends the conventional loss from pixel-only reconstruction to a joint appearance-motion prediction. The loss function is designed to supervise both aspects:

$$\mathcal{L} = \mathbb{E}_{[x_1, d_1], [x_0, d_0], y, t} \left[ \|u^+([x_t, d_t], y, t; \theta') - v_t^+\|_2^2 \right]$$

where $v_t^+$ represents the joint target velocity under the noise schedule, derived from both appearance and flow "ground-truths." This enforces simultaneous accuracy in reconstructing visual details and temporal evolution, leading to joint learning of appearance and motion priors (Chefer et al., 4 Feb 2025). The only architectural changes required are two linear layers: one for fusing appearance and motion at the input and one for outputting predictions for both modalities.

3. Inner-Guidance Inference Mechanism

A key innovation of VideoJAM is the Inner-Guidance mechanism, which is employed exclusively at inference. This mechanism leverages the model’s own evolving motion predictions during the sampling process as a form of dynamic, self-consistent guidance. The guidance is formulated as:

$$\tilde{u}^+ = (1 + w_1 + w_2) \, u^+([x_t, d_t], y, t; \theta') - w_1\,u^+([x_t, d_t], \varnothing, t; \theta') - w_2\,u^+([x_t, \varnothing], y, t; \theta')$$

where $w_1$ and $w_2$ are scaling factors for text and motion guidance, respectively. Unlike standard classifier-free guidance, the dynamic use of the model’s evolving motion output as a feedback control signal corrects possible inconsistencies and outliers in motion synthesis. Inner-Guidance is applied strictly during the initial denoising steps (specifically, the first half), as empirical evidence indicates that motion structure is primarily resolved in early steps (Chefer et al., 4 Feb 2025).

4. Evaluation and Comparative Performance

Assessment of VideoJAM is conducted using both human studies and automatic metrics provided by the VBench benchmark suite. Evaluation criteria are bifurcated into appearance (aesthetic quality, per-frame quality, subject consistency, background consistency) and motion (smoothness, dynamic degree). Results show that VideoJAM yields improvements in both motion smoothness and the "dynamic degree" of movement compared to its base models (e.g., DiT-4B, DiT-30B) and state-of-the-art proprietary competitors (e.g., CogVideo, Sora, Kling):

• Motion smoothness and dynamic plausibility scores are increased, confirming that the generated videos are both lively and physically coherent.
• Visual quality (aesthetic and per-frame) is equal to or better than baseline models.
• These trends are consistent across synthetic video datasets (VideoJAM-bench, Movie Gen), with VideoJAM also demonstrating robust performance in subjective human assessments (Chefer et al., 4 Feb 2025).

5. Architectural Adaptability and Implementation Considerations

VideoJAM is designed for minimal intervention in existing generative video model pipelines:

• Applicability is model-agnostic: the framework can be adapted to any video diffusion model with the addition of two lightweight linear layers (input fusion and output split).
• Fine-tuning is computationally efficient, as exemplified by usage on a DiT-30B model (256 A100 GPUs, 35,000 iterations at $256\times256$ resolution), and may require fine-tuning on as little as 3% of the original training dataset.
• No changes to training data or underlying model scaling are necessary. All modifications are strictly architectural and compatible with checkpoint restoration or continuation of previous training runs.
• The simplicity of the intervention facilitates rapid domain adaptation, experiment reproducibility, and deployment across a spectrum of generative model sizes (Chefer et al., 4 Feb 2025).

6. Applications and Implications

The explicit coupling of appearance and motion in VideoJAM has multiple direct implications:

• In text-to-video generation, the model yields samples with higher motion coherence and scene consistency.
• Content creation and editing workflows benefit from enhanced temporal smoothness that is critical in storytelling, animation, advertising, and virtual or augmented reality.
• Domain adaptation is streamlined: proprietary or legacy diffusion models can be retrofitted with VideoJAM's methodology to improve performance without the need to retrain on entirely new datasets.
• The framework establishes a foundation for subsequent work targeting more comprehensive priors, such as object interaction, temporal scene reasoning, and physics-influenced dynamics in video synthesis (Chefer et al., 4 Feb 2025).

7. Relation to Prior Work and Modular Multimedia Frameworks

VideoJAM’s component-based design resonates with historical frameworks for multimedia application development, wherein modular nodes or components are integrated via standardized interfaces. In this context, the framework’s modularity and portability echo principles exemplified in earlier distributed multimedia platforms, such as those leveraging XPCOM and NSPR for channel abstraction and dynamic runtime adaptation (0908.3082). Such design philosophies facilitate the seamless adaptation of VideoJAM’s principles to broader multimedia processing pipelines, including distributed or multi-modal media generation systems.

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In summary, the VideoJAM Framework effects a shift from conventional pixel-focused video synthesis by embedding an explicit motion prior through joint appearance-motion learning and dynamically guided inference. With its efficient, generic design, it delivers improved motion consistency and visual fidelity, validated by both quantitative benchmarks and human evaluation, and its structural simplicity ensures wide applicability for generative video modeling in both research and applied settings.