High-Resolution I2V & V2V Synthesis

Updated 23 August 2025

High-resolution I2V and V2V synthesis is the process of creating photorealistic video sequences with detailed spatial quality and consistent temporal dynamics from images or video inputs.
Methods integrate discrete latent representations, Transformers, diffusion models, and GANs to model both global structure and fine local details, enabling megapixel-level outputs.
Innovations such as progressive upscaling, motion conditioning, and domain-specific adaptations offer precise control and enhanced performance in applications like autonomous driving and event-based vision.

High-resolution image-to-video (I2V) and video-to-video (V2V) synthesis refers to the generative process of creating temporally consistent, photorealistic video sequences either from static images (I2V) or by transforming one video sequence into another (V2V), often at megapixel and above resolutions. This field is distinguished by its dual emphasis on spatial fidelity (high per-frame quality and detail) and temporal coherence (consistency and plausible motion across frames or views). Recent advancements have expanded the expressivity, scalability, and fidelity of generative models for these tasks, synthesizing video content at scales and with control precision previously unattainable.

1. Transformative Model Architectures and Frameworks

A foundational approach for high-resolution synthesis integrates context-aware compression with expressive generative modeling. "Taming Transformers for High-Resolution Image Synthesis" (Esser et al., 2020) demonstrated that a two-stage design—first using convolutional neural networks (CNNs) to encode images as discrete tokens (via VQGAN) and then synthesizing these codes autoregressively with a Transformer—can scale to megapixel image generation while maintaining global structure and local detail. The quantized latent representations drastically reduce sequence length and computational cost, making high-resolution content synthesis tractable with attention-based models.

Subsequent architectures extend this paradigm to complex visual and spatiotemporal domains:

Pure-likelihood VQGAN-Transformer hybrids synthesize high-resolution scene images conditioned on semantic layouts, eschewing auxiliary losses and intermediate mask generators, thus maximizing data likelihood to enforce global structure (Jahn et al., 2021).
Continuous-scale GAN generators enable “any-resolution” output via patchwise training conditioned on both global coordinates and scale, avoiding the fixed-size bottleneck and supporting flexible I2V/V2V synthesis by modeling spatial (and by extension, temporal) layouts as continuous functions (Chai et al., 2022).
Cascaded diffusion models (e.g., I2VGen-XL (Zhang et al., 2023)) and transformer-based latent diffusion models (e.g., VideoCrafter1 (Chen et al., 2023)) leverage multi-stage pipelines, dual encoders, and cross-attention to preserve semantic structure, style, and motion identity during video generation.

2. Explicit Temporal Modeling, Conditioning, and Control

State-of-the-art frameworks increasingly factorize video generation into explicit spatial and temporal modeling stages:

In Motion-I2V (Shi et al., 29 Jan 2024), a dedicated, diffusion-based motion field predictor first recovers pixel-wise displacement maps (optical flow fields) from a reference image and prompt. Subsequently, a motion-augmented temporal attention module injects warped features into a latent video diffusion model, ensuring temporally consistent detail propagation even over large motions or viewpoint shifts.
For cross-domain translation, I2V-GAN (Li et al., 2021) combines adversarially trained video-to-video generators, cycle-consistency with per-frame perceptual and style losses, and patch-level similarity constraints (via InfoNCE losses) to robustly translate between infrared and visible spectrums, maintaining spatial and motion coherence.
In FlowVid (Liang et al., 2023), imperfect optical flow is exploited “softly” rather than enforcing hard warping constraints: each subsequent frame is synthesized by conditioning on spatial maps and flow-warped references, which regularize temporal consistency within a diffusion-based editing architecture. The framework is flexible and can propagate intricate edits from strong single-frame I2I models through complex video sequences.

These approaches often support conditioning on both spatial (e.g., layouts, depth) and temporal (e.g., motion fields, prior frames) cues, enabling scene structure, semantic intent, or explicit user-specified trajectories to guide synthesis. ControlNet branches (Motion-I2V) further allow direct manipulation of object trajectories and motion regions with sparse annotation input, advancing fine-grained user control beyond textual prompts.

3. Upscaling, High-Frequency Detail, and Megapixel Generation

Rendering at ultra-high resolutions presents distinct challenges, especially rapid accumulation of high-frequency information, which can lead to repetitive artifacts. Methods such as CineScale (Qiu et al., 21 Aug 2025) introduce a progressive, multi-stage upscaling and denoising paradigm. The approach begins with low-resolution synthesis using a standard diffusion model, then progressively upsamples via tailored self-cascade upscaling:

At each upscaling stage, additional noise is injected and the denoising process is partially repeated, permitting the network to reconstruct finer structure.
Artifacts from misaligned frequency bands are mitigated via restrained use of dilated convolutions (applied only in certain blocks and scheduled by denoising timestep) and a Scale Fusion module. This module blends global soft-attention-derived context with local patch-based detail, fusing high-frequency components via low-pass filtering to suppress repetition.

When extending these principles to DiT (Diffusion Transformer)-based video models, specific adaptations such as NTK-RoPE positional embeddings and temperature scaling in the attention softmax are adopted to handle the extreme token counts inherent at very high resolutions. Minimal LoRA fine-tuning on a small set of 2K samples can adapt models for sharp video generation up to 4K.

4. Specialized Domains and View Synthesis

In practical domains, high-resolution I2V/V2V synthesis architectures are tailored to specific spatiotemporal, geometric, or multimodal requirements:

I2V-GS (Chen et al., 31 Jul 2025) addresses infrastructure-to-vehicle (I2V) view transformation for autonomous driving. Combining 3D Gaussian Splatting, adaptive depth warping, and Lidar-anchored monocular depth calibration with cascade diffusion-based inpainting, I2V-GS reconstructs photorealistic, geometrically accurate vehicle-perspective images from sparse, fixed roadside viewpoints. A confidence-guided loss leveraging cross-view perceptual similarity maintains consistency through challenging occlusions. On the RoadSight dataset, improvements of 45.7% in NTA-IoU, 34.2% in NTL-IoU, and 14.9% in FID over StreetGaussian are reported.
V2V (Lou et al., 22 May 2025) targets event-based vision, directly converting high-rate video streams to event-camera-inspired voxel grids via an efficient simulation that bypasses full event stream storage and enables training on orders-of-magnitude larger datasets. This enables I2V/V2V synthesis of temporally fine-detailed event domains with robust parameter randomization.

5. Data, Evaluation, and Impact

Scaling high-resolution I2V and V2V synthesis hinges on efficient, diverse data pipelines and strong benchmarks:

Large paired or unpaired datasets—with diverse modalities, such as RoadSight (multi-modal, synchronized, infrastructure viewpoints (Chen et al., 31 Jul 2025)) and IRVI (infrared–visible video pairs (Li et al., 2021))—are crucial for both training and reliable evaluation of these highly parameterized models.
Performance is evaluated by a range of metrics: FID (Fréchet Inception Distance) for appearance quality, SSIM and PSNR for structure, patch-FID for high-frequency fidelity, temporal consistency (via CLIP embedding cosine similarity), and task-specific geometric IoUs for view synthesis.
Efficiency advances (as in V2V’s 150× reduction in storage (Lou et al., 22 May 2025)) open the path for broader training and validation at high resolutions, previously constrained by computational and storage limits.

6. Comparative Summary and Open Directions

Approach	Core Innovation	Domain
Taming Transformers (Esser et al., 2020)	Discrete latent code + Transformer, sliding attention	High-res Image, extendable to I2V/V2V
CineScale (Qiu et al., 21 Aug 2025)	Progressive upscaling, frequency fusion, LoRA adaptation	I2V/V2V, up to 8K/4K
Motion-I2V (Shi et al., 29 Jan 2024)	Two-stage motion modeling, controllable synthesis	I2V/V2V
I2V-GS (Chen et al., 31 Jul 2025)	3D Gaussian Splatting, adaptive warping, confidence loss	View Synthesis (Driving)
V2V (Lou et al., 22 May 2025)	On-the-fly video-to-voxel simulation, storage-efficient	Event-based Vision

This field continues to advance rapidly, with contemporary work focusing on more precise and controllable motion synthesis, extension to highly diverse modalities (infrared, event, geometric), and effective management of computational constraints amid ever-increasing spatial and temporal resolution demands. A plausible implication is that further integration of motion modeling, upscaling mechanisms tailored to the peculiarities of video (such as cross-view consistency), and scalable, semi-supervised data curation will be central for next-generation high-resolution I2V and V2V synthesis systems.