Projected Latent Video Diffusion Models

• PVDMs are generative models that project raw video data into structured latent spaces using triplane or hybrid decompositions for efficient diffusion-based synthesis.
• They leverage autoencoders, video transformers, and tailored diffusion processes to achieve high temporal consistency, improved resolution, and reduced computational cost.
• Architectural variants enable multimodal extensions and scalability, supporting applications like text-to-video generation, video super-resolution, and dynamic motion control.

Projected Latent Video Diffusion Models (PVDM) are a class of generative models that perform diffusion-based video generation within a compact latent space obtained via structural projections—typically, 2D axis-aligned planes (triplanes) or hybrid decompositions—rather than in the original high-dimensional spatiotemporal pixel domain. This approach leverages architectures such as autoencoders and video transformers to achieve significant gains in efficiency, temporal consistency, and resolution, and can flexibly accommodate modality extensions (e.g., audio-video) or hybrid representations. PVDMs operate by first encoding video data into a low-dimensional latent manifold that factorizes spatiotemporal structure, then learning a probabilistic diffusion process in this space, which is reversed at inference to synthesize coherent, photorealistic, and long-range consistent videos. The procedural and architectural variants in the PVDM family include factorized 2D projections (Yu et al., 2023), integration with LDMs and temporal alignment modules (Blattmann et al., 2023), hybrid triplane–wavelet representations (Kim et al., 2024), and multi-modal extensions (Sun et al., 15 Nov 2025).

1. Latent Projection and Factorized Encoding Schemes

The foundational mechanism of PVDM is the projection of raw video data $x \in \mathbb{R}^{3 \times S \times H \times W}$ into a compact, structured latent representation via a video autoencoder. The most established schemes are:

• 2D Axis-Aligned Triplane Projection:

The encoder uses a 3D space–time transformer to yield an intermediate tensor $u \in \mathbb{R}^{C \times S \times H' \times W'}$, which is then collapsed along three axes to produce three 2D latent maps:

$$z^s \in \mathbb{R}^{C \times H' \times W'}, \quad z^h \in \mathbb{R}^{C \times S \times W'}, \quad z^w \in \mathbb{R}^{C \times S \times H'}$$

Each is obtained by small projection transformers (focusing on sequences along $S$, $H'$, or $W'$) (Yu et al., 2023, Kim et al., 2024). The triplane factorization reduces computational complexity from $O(SHW)$ to $O(HW + SH + SW)$ and supports highly scalable inference.

• Hybrid 2D Triplane + 3D Wavelet Volume:

In HVDM, the autoencoder first projects global context into triplanes as above and then extracts local volumetric and frequency information by applying a 3D discrete wavelet transform (DWT) to $x$, which yields eight subbands ($x_{ijk}$ for $i,j,k \in \{l,h\}$), encoded by separate 3D CNNs for low- and high-frequency content. These features are subsequently fused with the triplane latent via cross-attention, resulting in a composite latent $z$ (Kim et al., 2024).

• Orthogonal Decomposition for Multimodal Latents:

In ProAV-DiT, both video and audio (after Mel-spectrogram mapping) are encoded into three mutually orthogonal 2D latents per modality, constructed via axis-aligned projectors and enforced via mutual information regularization. These 2D latents are stacked as the channels of a 3D tensor processed by diffusion (Sun et al., 15 Nov 2025).

• Conventional LDM-style:

A regularized autoencoder maps each frame $$x \in \mathbb{R}^{3 \times \tilde{H} \times \tilde{W}}$$ to a latent $z \in \mathbb{R}^{C \times H \times W}$ using an $\ell_2$ pixel loss plus patch-GAN adversarial loss. For video, the encoder is applied temporally, and temporal alignment modules are used to enable coherent generation (Blattmann et al., 2023).

2. Diffusion Process in Projected Latent Space

PVDM employs a discrete-time DDPM in the projected latent space to facilitate efficient learning of complex video distributions:

• Forward Process:

Gaussian noise is incrementally added to latents according to:

$$q(z_t \mid z_{t-1}) = \mathcal{N}(z_t; \sqrt{1-\beta_t} z_{t-1}, \beta_t I)$$

with cumulative schedule $\bar\alpha_t = \prod_{i=1}^t (1-\beta_i)$, $$z_t = \sqrt{\bar\alpha_t}z_0 + \sqrt{1-\bar\alpha_t}\epsilon$$, $\epsilon \sim \mathcal{N}(0, I)$ (Yu et al., 2023, Blattmann et al., 2023, Kim et al., 2024, Sun et al., 15 Nov 2025).

• Reverse Process:

The denoising distribution is parameterized as:

$$p_\theta(z_{t-1} | z_t) = \mathcal{N}(z_{t-1}; \mu_\theta(z_t, t), \sigma_t^2 I)$$

where $$\mu_\theta(z_t, t) = \frac{1}{\sqrt{\alpha_t}}\left(z_t - \frac{\beta_t}{\sqrt{1-\bar\alpha_t}}\epsilon_\theta(z_t, t)\right)$$, with $\epsilon_\theta$ realized as a U-Net or Transformer in latent space (Yu et al., 2023, Kim et al., 2024, Sun et al., 15 Nov 2025).

• Loss Objective:

Training minimizes the expected squared deviation between sampled noise and the predicted noise:

$$L = \mathbb{E}_{z_0,\,\epsilon,\,t}\left[\|\epsilon - \epsilon_\theta(z_t, t)\|_2^2\right]$$

Training may include additional VLB, LPIPS, or GAN losses in the autoencoder (Kim et al., 2024).

• Arbitrary Length and Conditioning:

Conditional generation is enabled by inputting the latent of a previous segment or context latent as a conditioning vector. Losses are balanced between unconditional and conditional objectives (Yu et al., 2023, Kim et al., 2024).

• Sampling:

Inference employs the trained reverse process, typically with acceleration via DDIM sampling to reduce diffusion steps from 1000 to 100–250 (Yu et al., 2023, Blattmann et al., 2023).

3. Temporal Consistency and Alignment Mechanisms

Ensuring temporal coherence over synthesized video sequences is addressed by:

• Temporal Blocks in Latent Diffusion U-Nets:

For pretrained image LDMs, spatial blocks are frozen and temporal alignment blocks are interleaved at each U-Net stage. These operate via 3D convolutions (kernel size $(3,1,1)$) or temporal self-attention over $T$ frames, with their outputs linearly merged into the spatial activations using learnable scalars $\alpha_\phi^i$ (Blattmann et al., 2023). Temporal blocks are crucial for upsampling and for the removal of flicker in video reconstruction.

• Cross-shaped Attention and Multi-Head Attention:

In PVDM, after projection, triplane latents are denoised in parallel by a shared 2D U-Net, with cross-shaped attention layers inserted to fuse the features across $z^s$, $z^h$, $z^w$ (Yu et al., 2023). Transformers and multi-scale temporal self-attention enhance modeling of long-range dependencies (Sun et al., 15 Nov 2025).

• Fusion with Frequency-Matched Branches:

In hybrid models, cross-attention layers integrate global triplane and local wavelet latents iteratively, allowing each representation to be informed by the other (Kim et al., 2024).

4. Architectural Variants and Multimodal Extensions

Recent PVDM implementations exhibit architectural diversity:

• ST-DiT Backbone:

ProAV-DiT introduces a spatio-temporal diffusion Transformer (ST-DiT), which operates on stacked 2D latents (video and audio) in a small 3D tensor, with serialized temporal and spatial self-attention blocks and inter-group cross-modal attention. This permits bidirectional exchange between audio and video and supports efficient, synchronized generation (Sun et al., 15 Nov 2025).

• Hybrid Autoencoder (HVDM):

The HVDM structure marries transformer-based 2D triplane extraction with 3D DWT-based volumetric encoding, merging these by iterative cross-attention prior to diffusion. This structure empirically yields significant fidelity gains over pure 2D or 3D latent schemes (Kim et al., 2024).

• Latent Upsamplers and Super-Resolution:

Specialized diffusion models are trained to map low-resolution video latents to higher-resolution latents, using noise-augmented channel conditioning. Such upsamplers, coupled with temporal blocks, enable video super-resolution and can be fine-tuned independently (Blattmann et al., 2023).

• Backward Compatibility for Framewise Backbones:

PVDM frameworks can leverage pretrained image LDMs (such as Stable Diffusion), requiring only minor finetuning—mainly additional temporal alignment layers—thus extending high-quality image generators to the video domain (Blattmann et al., 2023).

5. Quantitative Performance and Empirical Evaluation

PVDM demonstrates state-of-the-art sample quality, efficiency, and scalability on multiple video generation benchmarks:

Model/Dataset	FVD↓ (16f)	KVD↓	IS↑	PSNR↑	LPIPS↓
PVDM-L, UCF-101	398.9	—	74.40	—	—
HVDM, UCF-101	303.1	23.6	—	34.00	0.038
PVDM-L, 128f UCF-101	639.7	—	—	—	—
ProAV-DiT, Landscape	80.3	7.3	—	—	—
ProAV-DiT, AudioSet	148.7	8.4	—	—	—

Empirical results (Yu et al., 2023, Blattmann et al., 2023, Kim et al., 2024, Sun et al., 15 Nov 2025) indicate:

• Substantially reduced memory and computation relative to pixel-space or naïve 3D-latent diffusion, e.g., >2$\times$ speedup and $\approx$5–11 GB less memory in the case of PVDM vs. VDM baselines (Yu et al., 2023).
• Hybrid 2D triplane/3D wavelet latents (HVDM) further reduce FVD/KVD and improve perceptual metrics and reconstructions: R-FVD drops from 27.03 (PVDM 2D-only) to 5.35 (hybrid) and LPIPS from 0.095 to 0.038 (Kim et al., 2024).
• Personalized text-to-video and conditional video generation preserve subject identity and support various downstream tasks such as image-to-video and video dynamics control (Blattmann et al., 2023, Kim et al., 2024).

6. Applications and Adaptability

PVDM's latent-diffusion framework is adaptable for:

• Unconditional and Conditional Video Generation:

Supports zero-shot synthesis, text-to-video, and personalized video generation, allowing for subject preservation and diverse content control (Blattmann et al., 2023).

• Long Video Generation via Autoregressive or Conditional Diffusion:

Ability to generate arbitrarily long videos by conditioning on previous clips (Yu et al., 2023, Kim et al., 2024).

• Video Super-Resolution:

Achieved by training latent upsampler diffusion models that elevate lower-resolution video latents to high-resolution outputs (Blattmann et al., 2023).

• Video Dynamics Control and Motion Guidance:

Enables explicit modulation of motion strength through inter-frame latent distances (Kim et al., 2024).

• Multimodal Audio-Video Synthesis:

Joint modeling of temporally aligned and semantically coherent video and audio, with strong benchmark results on FVD, KVD, FAD, and CLAP-Sim (Sun et al., 15 Nov 2025).

7. Significance and Comparative Analysis

PVDMs exhibit decisive advantages over pixel-space and conventional volumetric latent methods:

• Computational Efficiency:

Triplane and hybrid projections reduce latent size and computational burden while capturing key global and local dependencies.

• Spatio-Temporal Fidelity:

Hybrid models (2D/3D) merge the long-range modeling of transformers with the local detail retention of wavelet-aware CNNs, outperforming pure 2D or 3D approaches (Kim et al., 2024).

• Extensibility:

The projection–diffusion–alignment paradigm is compatible with diverse architectures (e.g., LDMs, diffusion Transformers, audiovisual pipelines) and generalizes well to longer sequences and additional modalities (Blattmann et al., 2023, Sun et al., 15 Nov 2025).

• Universal Framework:

The minimal additional cost required to adapt pretrained image LDMs (frozen spatial layers plus temporal alignment) streamlines the transition of generative models from still images to temporally consistent video (Blattmann et al., 2023).

These findings collectively position Projected Latent Video Diffusion Models as a foundational paradigm for efficient, scalable, and high-fidelity video synthesis, with significant implications for simulation, creative content creation, and multimodal generation tasks (Yu et al., 2023, Blattmann et al., 2023, Kim et al., 2024, Sun et al., 15 Nov 2025).