Video Inbetweening Framework

• Video inbetweening frameworks are computational models that generate intermediate frames from keyframes, ensuring temporal consistency and visual fidelity.
• They utilize filter-based CNNs, 3D residual networks, and diffusion Transformer architectures to blend spatial details with dynamic motion cues.
• Advanced control modalities, including sparse representations, dual-branch encoding, and multi-modal fusion, enhance applications in animation, digital content creation, and 3D scene rendering.

Video inbetweening frameworks encompass a spectrum of computational models, architectures, and control paradigms for synthesizing intermediate video frames given two reference images (or “keyframes”). The objective is to generate temporally consistent, visually plausible, and controllably dynamic transitions, with the field evolving from canonical filter‐based convolutional designs for line drawings (Yagi, 2017), through advanced tensor fusion in 3D convolutional models (Li et al., 2019), to contemporary Transformer-based, multi-modal, diffusion-driven systems (Tanveer et al., 9 Oct 2025). These frameworks have profound implications for animation, digital content creation, 3D scene rendering, and interactive video editing.

1. Core Architectural Paradigms

Video inbetweening architectures can be categorized into several principal classes:

• Filter-Based Multi-Scale CNNs: Early work deploys dual-stream convolutional neural networks, where low- and high-resolution branches process global structure and fine local details independently. Layer-wise operations typically alternate convolutions and ReLU activations, often with bilinear upsampling for resolution matching. For each branch, feature updates are given by formulations such as

$$y_{u,v,c}^{(0,\ell+1)} = b_c^{(0,\ell)} + \sum_{d_u=-1}^{1} \sum_{d_v=-1}^{1} \sum_{i=1}^{n_c^{(0,\ell)}} W_{c,d_u,d_v,i}^{(0,\ell)} \, x_{s_\ell \cdot u + d_u,\, s_\ell \cdot v + d_v,\, i}^{(0,\ell)}$$

enhancing aggregation of topological detail and overall continuity while obviating explicit correspondence computation (Yagi, 2017).

• Fully Convolutional 3D Video Generators: RNN-free architectures employ deep stacks of 3D residual blocks and 2D encoders for latent representation learning. Stochastic fusion mechanisms, with gating parameters modulated by injected noise, blend start and end frame information at each layer:

$$z_{\text{in}}^{(l)} = g_s^{(l)} \cdot E(x_s) + g_e^{(l)} \cdot E(x_e) + \max(0, 1-g_s^{(l)}-g_e^{(l)}) \cdot z^{(l-1)} + n^{(l)}$$

where $g_s$, $g_e$ are gating functions parameterized by temporally convolved, noise-conditioned signals. Adversarial training facilitates sample diversity and preserves consistency (Li et al., 2019).

• Diffusion Transformer-Based Designs: State-of-the-art approaches map video frames into spatio-temporal patch tokens via a 3D VAE, and guide the denoising transformer with embedded multi-modal control signals. Content and motion cues are processed by separate generator branches, yielding dual channelwise embeddings for the Transformer denoiser. Loss optimization follows a noise prediction paradigm:

$$\mathcal{L}_{\text{diff}} = \|\hat{\epsilon} - \epsilon\|^2_2$$

enabling granular control via multi-modal inputs (trajectories, depth, text, masks, etc.) (Tanveer et al., 9 Oct 2025).

2. Control Modalities and Sparse Representations

Contemporary frameworks emphasize controllability and flexible input modalities:

• Sparse Point-Based Representation: Optical flow, depth, and region control signals are abstracted to sparse RGB points or heatmaps, compatible with the VAE-encoded latent structure of DiT generators. Points are spatially expanded with Gaussian or disk filters depending on the intended cue—minimizing extraneous spreading of depth and ensuring diagnostic trajectory coverage.
• Dual-Branch Encoding: Control signals are segregated into dedicated content (region, keyframe, mask) and motion (trajectory, depth) branches, each embedded and then channel-concatenated for Transformer processing—reducing semantic-motor interference and improving localization of movement (Tanveer et al., 9 Oct 2025, Tanveer et al., 17 Dec 2024).
• Multi-modal Fusion: Systems such as MotionBridge support text prompts, guide pixels, keyframes, masks, and user-defined trajectories, with curriculum training to phase the learning of different control types and avoid mode collapse or ignored signals (Tanveer et al., 17 Dec 2024).

3. Specialized Application Domains

• Cartoon/Anime Production: Dedicated frameworks unify inbetweening and colorization, leveraging sparse sketch injection mapped to temporal positions via position encoding and low-rank adapters that modify only spatial features while preserving temporal prior (Li et al., 14 Aug 2025). This post-keyframing process reduces manual efforts and maintains stylistic fidelity across interpolated and colored frames.
• Motion Synthesis for Kinematic Characters: Mixture-of-Experts architectures regulate motion via expert blending networks conditioned on phase, style, root trajectory, and time-to-arrive signals. These networks routinely evaluate physical plausibility with kinematic consistency and L2 joint error metrics—attaining real-time synthesis rates on mainstream GPUs (Chu et al., 30 Sep 2024, Starke et al., 2023).
• 3D Dynamic Scene Generation: Hierarchical approaches for generative inbetweening of 4D scenes (3D+motion) decompose long-range dynamics into manageable fragments, reconstructing geometry via Gaussian Splatting and smoothing motion fields via rigid transformation regularization and multi-view diffusion (Nag et al., 11 Apr 2025, Kim et al., 22 Sep 2025).

4. Temporal Consistency, Sampling, and Optimization Strategies

Temporal smoothness is crucial in inbetweening:

• Iterative, Multi-stage Inference: Filter-based methods recursively apply the CNN between pairs of frames to produce denser temporal interpolation (e.g., quarter frames) (Yagi, 2017).
• Bidirectional Sampling and Fusion: Bounded generation techniques employ Time Reversal Fusion, running denoising both forward (start-conditioned) and backward (end-conditioned). At every timestep, outputs are averaged with adaptive weights:

$$x_t^n = \alpha_n \cdot x_{t,s}^n + (1-\alpha_n) \cdot x_{t,e}^{N-n-1}$$

synchronizing both boundary constraints and enhancing 3D view synthesis and looping capabilities (Feng et al., 21 Mar 2024, Zhu et al., 16 Dec 2024).

• Enhanced End-Frame Constraint Injection: Sci-Fi (Chen et al., 27 May 2025) identifies asymmetric constraint strengths in typical diffusion models and introduces EF-Net, which encodes the end frame into temporally adaptive frame-wise features. By expanding temporal coefficients over all frames and fusing them via transformer blocks and MLPs into the main pipeline, start and end frames exert symmetric influence, producing more harmonious transitions.

5. Quantitative Performance, Experimental Methodologies, and Benchmarks

• Metrics: Performance is assessed via Fréchet Video Distance (FVD), LPIPS, SSIM, PSNR, VBench, as well as specialized “Motion” metrics using trajectory matching (Tanveer et al., 9 Oct 2025, Tanveer et al., 17 Dec 2024). Benchmarks such as DAVIS, UCF (sports action), HumanSloMo, and PKBench rigorously test fidelity, consistency, and generalization across animation, real-world motion, and cartoon domains.
• Quality and Robustness: Dual-branch architectures and curriculum training strategies consistently outperform single modality and single-branch models in motion localization, artifact suppression, and adaptation to large or rapid changes in object shape.

6. Limitations, Extensions, and Future Directions

Despite advances, several open challenges persist:

• Ambiguity in Interpolation Path: Methods relying solely on start/end frame conditions often struggle with large motion gaps. Frame-wise condition-driven approaches (matched lines, pose skeletons, non-linear interpolation) (Zhu et al., 16 Dec 2024) and curriculum training mitigate but do not eliminate ambiguity.
• Topological Changes and Scanning Artifacts: Filter-based approaches may falter where line correspondences or topologies vary drastically (e.g., occlusions, merges/splits in line art) (Yagi, 2017).
• Model Scalability and Constraint Integration: Scaling to higher-capacity models (e.g., Wan2.1-FLF2V-14B, CogVideoX-5B-I2V) and incorporating additional control signals (e.g., explicit motion, depth, region-specific cues) remain active research directions (Chen et al., 27 May 2025, Tanveer et al., 9 Oct 2025).
• Practical Workflow Integration: Applications in cartoon and 3D scene synthesis benefit from region-wise control and sparse input support, motivating further investigation into adaptive sketch and mask injection and more seamless integration of user- or data-driven controls (Li et al., 14 Aug 2025).
• Automatic Trajectory Estimation: Autopilot modes for keypoint tracking and trajectory updating (e.g., SIFT/Co-Tracker methods in Framer (Wang et al., 24 Oct 2024)) simplify usage but remain bounded by feature correspondence estimation quality and variability in scene semantics.

7. Applications and Societal Impact

Video inbetweening frameworks serve roles in animation and cartoon production, time-lapse and slow-motion synthesis, interactive video editing, real-time VR, and 3D content creation. With expanding control modalities and improved fidelity, these systems democratize dynamic content generation, accelerate production pipelines, and enable creative authoring for both professionals and researchers. The ongoing fusion of stochastic, deterministic, and multi-modal paradigms continues to redefine the limits and versatility of computational video synthesis.