Self-Keyframing: Continuous Motion Synthesis

• Self-keyframing is a technique that automatically identifies semantically significant keyframes from motion data to guide the interpolation of intermediate frames.
• It employs transformer-based keyframe encoding and intermediate token generation to construct a continuous latent manifold, overcoming limitations of traditional linear methods.
• This approach enhances motion realism and robustness across applications such as animation, video summarization, and robotic control through improved loss functions and context-driven synthesis.

Self-keyframing refers to the automatic discovery and utilization of keyframes from a given sequence, where the keyframes serve as critical anchors for synthesizing intermediate representations in motion or video data. This approach is founded on the principle that by learning to distill the information inherent in sparse keyframes and their interrelations, one may build continuous, data‐driven latent manifolds that reliably interpolate intermediate frames or motions while preserving global context and fine-grained details.

1. Fundamental Concepts and Motivation

Self-keyframing emerged from the need to overcome limitations of traditional interpolation methods such as linear interpolation (LERP) and fixed mask-token approaches. In settings where temporal continuity and precise motion dynamics are critical—for example in 3D human motion animation, video keyframe extraction, and robotic world modeling—the direct interpolation of full sequences often leads to artifacts or trivial local minima during training. Self-keyframing instead emphasizes the extraction of semantically significant key poses, which in turn guide the dense interpolation of “inbetween” frames via learned latent representations. By constraining intermediate token generation solely using these keyframes as context, models are forced to capture the global dynamics and spatial structure of the underlying motion rather than overfit to local redundancy.

2. Methodological Approaches

A common framework for self-keyframing comprises three main stages:

1. Keyframe Encoding: The sparse input keyframes, for example a set $K = \{x_0, x_{t_1}, x_{t_2}, \dots, x_{N-1}\}$, are linearly projected into a higher-dimensional latent space while preserving spatial and temporal precision via concatenated positional encodings. The output tokens, often denoted as $$\Phi^{\text{key}(K)} = \{\phi_0, \phi_{t_1}, \dots, \phi_{N-1}\},$$ serve as fixed context for subsequent processing.
2. Intermediate Token Generation: For each time-step where a keyframe is not provided, a separate transformer is employed to generate a latent token $m_t$ by conditioning on the keyframe subspace. This stage is formulated as $$m_t = \Phi^{\text{imd}}(t \mid \Phi^{\text{key}(K)}),$$ ensuring a smoothly varying motion manifold that implicitly guides the synthesized trajectory.
3. Motion Synthesis: All tokens—both keyframe-derived and generated intermediates—are concatenated and further refined using a feedforward network (or an additional transformer) to produce the final synthesized sequence of motions or video frames. The synthesis is defined by a conditional formulation wherein keyframe tokens may be processed by a standard feedforward network while intermediate tokens are used directly, as summarized by

$$\hat{m}_t = \begin{cases} \text{FFN}(\phi_t), & \text{if } t \text{ is a keyframe}; \ m_t, & \text{otherwise}. \end{cases}$$

This staged design encourages the model to capture both high-level motion structure and fine temporal variations without falling into overfitting on local trivialities.

3. Mathematical Formulation and Learning

The self-keyframing approach learns a mapping from temporal indices and keyframe embeddings to a continuous latent manifold. Training utilizes a multi-term loss function that balances local reconstruction errors (such as differences in root position and joint rotations) and global consistency losses computed through forward kinematics. A representative loss function is given by

$$L = \alpha_l (L_{\text{root}} + L_{\text{quat}}) + \alpha_g (L_{FK_p} + L_{FK_q}),$$

where each term is computed as an ℓ1 error averaged over the sequence. In addition, special normalization steps such as replacing LayerNorm with RMSNorm and applying sequence-level re-centering are introduced to stabilize training when handling continuous attributes.

Models based on this method typically employ transformer architectures with context-guided attention, enabling the intermediate token generation to be restricted solely by the keyframe context. This strategy leads to smooth, realistic inbetween motion that is robust to long keyframe intervals and complex actions.

4. Comparisons to Traditional Approaches

Traditional techniques for motion interpolation often rely on linear methods or the insertion of fixed mask tokens. Such methods tend to produce results that are either overly simplistic or fall prey to trivial local minima, especially when keyframes are sparsely provided. In contrast, self-keyframing:

• Replaces ad-hoc interpolation techniques with a continuous, learned latent manifold.
• Avoids reliance on poorly conditioned linear seeds by generating intermediate tokens in a context-restricted manner.
• Leverages full sequence context to achieve robust continuity and physical plausibility.

Empirical evaluations using metrics such as L2 error for positions (L2P), L2 error for rotations (L2Q), and the normalized power spectrum similarity (NPSS) have demonstrated significant improvements in interpolation accuracy and visual similarity to ground truth motions when employing self-keyframing.

5. Applications and Impact

Self-keyframing has been applied in various domains:

• 3D Motion Interpolation: In animation and character motion synthesis, self-keyframing enables the generation of continuous and physically plausible motion trajectories from a few input poses.
• Video Keyframe Selection: In video summarization and keyframe detection, self-keyframing techniques can improve the selection of user-adaptive thumbnails by identifying semantically rich moments.
• Robotic World Modeling: World models for robotics use self-keyframing to concentrate computation on semantically significant key states, thereby improving inference speed and physical plausibility.
• Visual Imitation Learning: For behavioral cloning in partially observable environments, upweighting keyframes corresponding to expert changepoints addresses the “copycat problem” seen when models overly rely on recent observations.

By integrating self-keyframing techniques, state-of-the-art methods have consistently demonstrated improvements in efficiency, robustness, and overall performance, setting new standards for transformer-based motion interpolation and related tasks.

6. Future Directions

Future research in self-keyframing may explore enhanced multi-modal conditioning, where additional modalities (such as text or audio) further refine keyframe selection and generation. There is also active interest in scaling self-keyframing methods to more complex sequences and longer time horizons, as well as integrating adaptive keyframe selection into reinforcement learning pipelines for robotic control. The continued evolution of transformer architectures and diffusion models offers promising avenues for further improving the fidelity and controllability of generated motion sequences.

Self-keyframing thus represents a robust, data-driven paradigm for bridging sparse human-specified input with dense, continuous motion synthesis, with applications ranging from animation production to robotic planning.