Neural Video Representation with Coherent Modulation (NVTM)

• NVTM is a neural video modeling technique that enforces temporal coherence by adaptively modulating latent features based on temporal correspondences.
• It leverages spatial-temporal decomposition to reduce redundancy and accelerate encoding compared to traditional frame-wise methods.
• NVTM achieves state-of-the-art reconstruction and compression performance, making it valuable for applications like super-resolution, interpolation, and inpainting.

Neural Video Representation with Temporally Coherent Modulation (NVTM) denotes a class of approaches in video modeling and compression that explicitly encode, exploit, or enforce temporal coherence within neural representations. NVTM encompasses frameworks that move beyond static per-frame or purely spatial representations, introducing mechanisms to modulate neural features according to temporal correspondences, flows, or structural cues—thereby enhancing parameter efficiency, reconstruction quality, and the stability of downstream applications such as super-resolution, interpolation, and compression.

1. Principles and Motivations

Neural implicit representations for video, or INR-based methods, model a video as a continuous or high-level function of spatial and temporal coordinates. Conventional methods—such as NeRV, NVP, Instant-NGP, or frame-wise decoders—either treat time as an uninformed axis or replicate parameters redundantly along the temporal dimension. This can lead to parameter inefficiency, suboptimal compression, and inadequate modeling of dynamic video content. NVTM frameworks are motivated by the observation that temporal coherence—the smooth, consistent evolution of scene content over time—offers a powerful inductive bias for more efficient neural video modeling. By explicitly modulating latent features with temporally aligned correspondences, such frameworks reduce redundancy, better encapsulate video dynamics, and improve both computational and representational efficiency (2505.00335).

2. Core Methodology: Spatial-Temporal Decomposition and Modulation

At the core of NVTM is the decomposition of the video’s spatio-temporal volume into aligned, lower-dimensional latent representations, modulated in accordance with temporal correspondences:

• Spatial-Temporal Decomposition: The video’s coordinate space $(x, y, t)$ is partitioned into temporal units, such as GOPs (Groups of Pictures). For each GOP, an alignment flow network predicts a mapping from every $(x, y, t)$ tuple to a canonical spatial coordinate at the GOP reference (“keyframe”).

$\text{Flow}_{t \to t_k}(x, y) = F(x, y, t)$

$$(x_k, y_k) = (x, y) + \log{(t-t_{k})} F_{\mathcal{H}(t)}(x, y)$$

The mapping is adaptively normalized to the grid for subsequent lookup.

• Latent Grid Modulation: Each GOP has an associated 2D latent grid $G_k$ storing learnable feature vectors. These grids are accessed at the temporally aligned spatial coordinates $(x'_k, y'_k)$ to yield a latent code:

$z_{xyt} = G_k(x'_k, y'_k)$

This latent code then modulates the layers of the main neural decoder

$(r, g, b) = \mathcal{M}_{\theta(z_{xyt})}(x, y, t)$

encoding temporal consistency by ensuring that corresponding regions across frames share neural feature modulations.

• Temporally Corresponding Modulation: Through this alignment, temporally corresponding pixels are processed with shared and smoothly varying latents, enforcing temporal coherence and reducing inter-frame artifact propensity.

This approach distinguishes itself from generic 3D grid-based INR methods (which assign a latent value to every spatio-temporal voxel) by instead imposing adaptive alignment and latent sharing over time, yielding lower redundancy and higher modeling fidelity (2505.00335).

3. Parameter Efficiency and Encoding Speed

Parameter efficiency is a principal advantage of NVTM-style architectures:

• Parameter Count: By aligning grids and reusing latent features, NVTM methods reduce the total number of learnable parameters. Empirically, NVTM achieved 10% fewer parameters, yet +1.54 dB (UVG) and +1.84 dB (MCL-JCV) PSNR improvement over prior grid-type INRs, substantiating the reduction of redundancy ([Table 2], (2505.00335)).
• Encoding Speed: NVTM processes temporally corresponding spatial regions in parallel, allowing batchwise updates and faster convergence. For matched quality, NVTM was reported as over 3 times faster than NeRV-style approaches during encoding ([Table 3], (2505.00335)).
• Robustness to Temporal Axis Size: While shrinking the temporal extent of 3D grids quickly degrades reconstruction quality in older methods, NVTM’s temporal alignment enables robust performance for longer sequences and higher frame rates without proportional parameter growth.

4. Reconstruction Quality and Applications

NVTM’s temporally modulated representations yield superior results for a range of video-centric tasks:

• Video Reconstruction: Achieves the highest PSNR and lowest LPIPS among parameter-efficient methods for dynamic content.
• Video Super Resolution: Supports decoding at higher spatial resolutions than training, reflecting the strengths of coordinate-based INRs for continuous outputs. Empirically, NVTM outperforms other grid-based methods in super-resolution (e.g., 35.82dB for 2x upscaling in Table 4 (2505.00335)).
• Interpolation and Inpainting: The capacity to decode at arbitrary time points enables high-quality frame interpolation (up to 6dB higher PSNR over prior art for interpolated frames) and effective inpainting of masked video regions, as the temporally coherent latents retain contextual structure ([Sec. 4], [Fig. 5a], (2505.00335)).
• Compression: NVTM’s compact latent grids are well-suited for entropy coding (e.g., with HEVC), leading to compression rates and visual quality competitive with or exceeding H.264/HEVC and strong INR baselines under matched bitrates (see [Fig. 5b], (2505.00335)).

5. Architectural Comparison

NVTM can be contrasted with other INR and frame-wise approaches as follows:

Method	Parameter Efficiency	Encoding Speed	Temporal Consistency	Compression/Quality
3D Grids (NVP, Instant-NGP)	Low (dense in t)	High	Implicit, weak	Moderate
NeRV-style (Frame-wise INR)	Moderate	Low	Strong (frame-level, slow)	High
NVTM (Ours)	High	Very High	Explicit (modulation, flow-alignment)	SOTA

This arrangement demonstrates that NVTM’s dynamic alignment and modulation allow for superior trade-offs among compactness, temporal stability, and representational power.

6. Implementation Guidance and Considerations

• Modulation Network: It is essential to carefully design the modulation mapping $\mathcal{M}_{\theta(z_{xyt})}$, as it must sufficiently inject latent variation while sharing parameters across frames for coherence.
• Alignment Flow Learning: The alignment network $F$ should be trained with constraints or regularization to prevent fold-overs and ensure bijective mappings in the spatial domain.
• Grid Organization and Compression: Organizing the per-GOP 2D latent grids as video-like data allows leveraging standard codecs for grid compression, further reducing overall bitrate.
• GOP Size and Trade-offs: Smaller GOPs increase temporal adaptivity at the expense of more grids, while larger GOPs increase latent reuse but may underfit fast temporal changes. Adaptive GOP partitioning may be beneficial for long or diverse-content videos.
• Scalability: NVTM is robust to long sequences and adapts to varied bitrates and spatial scales, supporting deployment in various video processing pipelines, storage, or streaming scenarios.

7. Impact and Extensions

NVTM represents a significant advancement in neural video representation by embedding temporally coherent modulation directly into the latent space. The approach yields state-of-the-art results in terms of both reconstruction accuracy and computational efficiency across key video tasks (reconstruction, super resolution, frame interpolation, inpainting, and compression), outperforming prior grid-based and implicit representation frameworks while matching or surpassing traditional codecs under practical settings (2505.00335).

A plausible implication is that future developments may draw on NVTM’s core principle—dynamically aligned low-dimensional modulation—for even more scalable or real-time neural video models, or to further enable applications such as semantic video editing, adaptive bitrate streaming, and temporally-aware generative video modeling.

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References to tables, formulas, and section headings are derived from the cited paper (2505.00335); no content is inferred beyond the source data.