Spatio-Temporal Proxy Nodes

• The paper introduces a proxy-node paradigm that decouples shape and texture, enabling efficient video reconstruction and controllable editing.
• It employs hierarchical semantic layers and dynamic supplementation to maintain spatio-temporal consistency despite motion, occlusion, and tracking errors.
• The approach outperforms dense correspondence methods by offering parameter efficiency and resilient editing for complex, real-world video sequences.

Spatio-temporally consistent proxy nodes are sparse, dynamically maintained spatial anchors that robustly encode both geometric structure and appearance of video objects across time, supporting high-fidelity reconstruction and temporally stable editing even under severe motion, occlusion, or tracking inaccuracies. This vectorized proxy-node paradigm addresses the longstanding weaknesses of pixel-level correspondence approaches, enabling stable video representations that remain coherent under challenging real-world conditions such as large motion, occlusion, long video duration, and viewpoint changes (Chen et al., 14 Oct 2025).

1. Proxy Node Definition and Role

The proxy nodes are spatially distributed, semantically anchored control points defined per semantic layer of a video. At initialization (typically in the first frame of a given layer), nodes are detected via a combination of edge extraction—using vectorization tools like VTracer for boundaries—and internal feature-based sampling (e.g., via the Sobel operator), ensuring both boundary and interior representativity. Each node encodes:

• Spatial position, capturing the local geometric structure.
• A learnable texture code, storing per-node appearance information.

Once initialized, proxy nodes are propagated forward and backward across frames, serving as persistent “anchors” for tracking object/scene content over time. These anchors are not reliant on dense pixel-level tracking or optical flow, making the representation resilient to tracking drift and occlusion. Proxy nodes form the basis for spatial vectorization (decomposition into editable semantic layers) and for driving implicit neural image reconstruction with a dramatically reduced parameter count compared to pixel-wise models.

2. Hierarchical and Semantic Layer Structure

Proxy nodes are organized hierarchically by semantic layers obtained using advanced segmentation methods (e.g., Grounded SAM2). Each layer may correspond to a distinct object or scene region, allowing the representation to capture both multi-object context and intra-object structure. Within a layer:

• Edge-based control points outline the boundary geometry for robust contouring.
• Gradient-based internal sampling provides anchor coverage for texture consistency and detailed region reconstruction.

The hierarchical design stabilizes the representation in several ways:

• It isolates error propagation—each layer’s proxy nodes evolve independently.
• It prevents foreground-background blending or cross-object contamination, which is critical for maintaining compositional editability.
• It supports multi-scale representation, with both coarse structural anchors and dense fine-detail control nodes as needed.

3. Dynamic Update and Supplement Mechanism

To address long-term motion, occlusion, and tracking inaccuracies, the proxy node set is dynamically adjusted over time. After forward propagation (e.g., via sparse nearest-neighbor or Kalman-based tracking), a dynamic node supplementation procedure identifies pixels in current frames that have become spatially distant (above threshold ε_d) from any existing proxy node. These “gaps”—which arise due to occlusion, rapid deformation, or tracking loss—trigger insertion of new proxy nodes at relevant locations. This supplementation is iteratively bi-directional (applied both forward and backward in time), ensuring that no region remains underrepresented in any frame.

This dynamic adaptation leverages both spatio-temporal priors—e.g., expected smoothness of node motion across consecutive frames—and robustifies the representation against cumulative error or drifting. As a result, geometric and appearance information persists even under severe challenges where dense trackers would fail.

4. Decoupled Shape and Texture Encoding

A central property is the explicit decoupling of shape (geometry) and texture (appearance):

• Node positions P encode dynamic geometry: Pᵢ ∈ ℝ^g×2n, with g nodes over n frames.
• Texture codes F encode per-node semantic appearance: Fᵢ ∈ ℝ^g×c, where c is feature dimension.

This separation is exploited for controllable editing. Shape can be manipulated (e.g., spatial warping, segmentation, or in-painting) while retaining consistent texture; conversely, appearance edits (e.g., colorization, drawing) can be performed on the texture codes, with the geometry held fixed. During implicit neural reconstruction, these two channels are combined via barycentric interpolation (using Delaunay triangulation of proxy nodes per-frame) to form per-pixel features, before mapping to RGB values through a coordinate-conditioned MLP with high-frequency encodings (similar to NeRF paradigms):

$$f_{t,x} = \sum_{k=1}^{3} \lambda_{t,x}^{i,k} F^{i,k},\ \hat{I}_{t,x} = \phi_{\theta}(\mathcal{U}_{freq}([f_{t,x}, t, x]))$$

where $\lambda$ are barycentric weights for the triangle enclosing pixel x.

5. Applications: Reconstruction, Editing, In-Painting

The hierarchical, proxy-node-based representation enables a suite of robust, high-fidelity video processing applications:

Application	Mechanism	Remarks
Video Reconstruction	Proxy nodes as anchors, implicit neural decoder	High PSNR, low LPIPS, and high SSIM even under occlusion
In-Painting	Discarding unwanted foreground proxy nodes, supplementing background nodes	Strong temporal consistency in completed areas
Consistent Editing	Edits on keyframe node textures/positions, propagated throughout	Stable for nonrigid objects and appearance changes
Interpolation	Smooth spatial/temporal interpolation on proxy trajectories	Efficient frame or resolution upsampling

Unlike dense correspondences, the sparse proxy node approach avoids error explosion over long sequences, and the dynamic supplementation mechanism ensures representations adapt to evolving scene content. This supports tasks (e.g., in-painting, VR compositing, content-aware video synthesis) that demand persistent temporal coherence and robust feature anchoring.

6. Advantages, Limitations, and Implications

Main advantages of this proxy-node design are:

• Parameter efficiency: Achieves state-of-the-art reconstruction and editing accuracy with fewer parameters than prior approaches (e.g., VeGaS, CoDeF).
• Robustness: Insensitive to small tracking errors, occlusion, or motion outliers, due to dynamic node updating and independence from dense optical flow.
• Fine-grained control: The decoupled encoding naturally supports selective object editing, semantic vectorization, and smooth interpolation.

A plausible implication is that this approach can generalize to broad scenarios requiring temporally consistent video representations; however, the efficacy depends on accurate initialization of semantic segmentation and the quality of dynamic node supplementation. Potential limitations may arise in highly textured or extremely cluttered scenes, where proxy node coverage could require denser sampling to avoid spatial artifacts.

In summary, the hierarchical, spatio-temporally consistent proxy node paradigm introduces a resilient foundation for next-generation video representation, delivering robust structure and texture anchoring that enables stable, efficient, and controllable video processing applications in dynamic environments (Chen et al., 14 Oct 2025).