TV2TV: Unified Video & Transmission Framework

• TV2TV is a multifaceted framework that integrates interleaved text planning with pixel-level video synthesis, enabling enhanced semantic control and fidelity.
• It employs a dual-tower Mixture-of-Transformers architecture with shared self-attention to achieve superior prompt alignment and measurable gains in experimental benchmarks.
• Additionally, TV2TV extends its capabilities to SDR-to-HDR conversion and P2P streaming overlays, demonstrating advanced processing techniques and system-level optimization.

TV2TV refers to multiple technical domains across modern video and television research, each converging on advanced processing or transmission between television signals, or on generative video systems that interleave modalities or transmission paradigms. The most prominent, contemporary usage leverages the term TV2TV for a unified generative modeling framework for interleaved language and video generation (Han et al., 4 Dec 2025), while prior literature utilizes TV2TV to refer to tasks such as SDRTV-to-HDRTV up-conversion (Chen et al., 2023, Xu et al., 2023) and peer-to-peer overlay streaming (Biernacki, 2012). This entry synthesizes the main technical advances, methodologies, and quantitative results from all three principal TV2TV interpretations.

1. Interleaved Language-Video Generation: TV2TV Framework

The most recent and significant instantiation of TV2TV is the design of an omni video-text generative model that decomposes video synthesis into an interleaved process of text planning ("think in words") and pixel generation ("act in pixels") (Han et al., 4 Dec 2025). The principal motivation is to address the limitations of conventional video generation pipelines (e.g. T2V, Think2V), which fail to capture high-level semantic reasoning and struggle with complex branching content.

TV2TV achieves this by generating short, natural-language plans at every content inflection (e.g. "the surfer leans into an upward turn") and then synthesizing the next contiguous video frame segment, conditional on those plans. This decoupling offloads abstract temporal reasoning onto a LLM subcomponent, reducing the entropy of the pixel-level video model and substantially improving both alignment to prompts and user controllability.

2. Mixture-of-Transformers (MoT) Architecture

TV2TV is built as a two-tower Mixture-of-Transformers (MoT), with one tower for text (language modeling) and one for video (pixel synthesis). Both towers share a common global self-attention mechanism over the entire interleaved sequence, but retain modality-specific projections and feed-forward networks. The architecture operates in alternation:

• Text input: Tokens $x^{\text{txt}}$ are projected and routed through the "language" tower.
• Video input: Groups of frames (packaged as continuous VAE latents $x^{\text{vid}}$) are processed by a U-Net downsampler and routed through the "video" tower.

Stacked MoT layers ensure that each modality-specific Q, K, V parameterization attends over the full sequence with hybrid, causal/block-causal attention masking. Output heads deliver either logits for next-token prediction or denoised latents for the next video chunk.

3. Training, Losses, and Inference Workflow

TV2TV is trained end-to-end with a weighted sum of cross-entropy language modeling loss ($\mathcal{L_\text{txt}}$) and flow-matching mean squared error loss on the video latents ($\mathcal{L_\text{vid}}$):

$$\mathcal{L} = \lambda_\text{txt} \mathcal{L}_\text{txt} + \lambda_\text{vid} \mathcal{L}_\text{vid}$$

Inference proceeds autoregressively, with special BOF ("begin-of-frame") and EOF tokens controlling when the system alternates between text planning and video synthesis. At BOF, the system invokes an ODE/rectified flow solver to sample video latents, optionally with classifier-free guidance.

TV2TV’s context window is extensible—by sliding the context and retaining the trailing half of each interleaved window, it can synthesize arbitrarily long videos.

4. Controllability via Text Interventions

TV2TV uniquely empowers users to steer the generative process at arbitrary points by making textual interventions. Because planning text is emitted prior to every video chunk, the generation can be paused, the plan tokens overwritten, and the subsequent video will condition directly on the new user-specified guidance. This enables unprecedented fine-grained control over the video trajectory, something unavailable in models that treat video generation as a monolithic mapping from prompt to pixels.

5. Experimental Results and Quantitative Benchmarks

TV2TV was evaluated both in synthetic (CS:GO gameplay) and real-world (YouTube sports) video domains.

• CS:GO setup: 95 hours (16 FPS) with interleaved ground-truth text actions. For a 3B MoT (28 layers):
  • TV2TV videos were preferred to T2V videos in 91% of human pairwise tests (1% reversed, 8% ties).
  • Intervention correctness: TV2TV (78%) markedly exceeds Think2V (59%).
• Sports video setup: 8K hours with VLM-generated captions; TV2TV (8B, 32 layers) compared to SOTA T2V (Cosmos2, MAGI-1, WAN-2.2):
  • TV2TV won on prompt alignment, real-world fidelity, holistic preference.
  • Holistic preference: TV2TV 54% vs T2V (35%), 53% vs Think2V (41%), with ∼20-point improvement in prompt alignment.

6. Peer-to-Peer TV2TV Streaming Overlays

TV2TV in the context of BitTorrent-based P2P TV overlays refers to distributed, live-channel sharing overlay networks, modeled as event-driven systems (Biernacki, 2012). The architecture consists of:

• Peer management with churn via random join/leave processes and central tracker.
• Overlay maintenance using bidirectional neighbor lists, neighbor quality adaptation, and per-connection limits.
• Video chunk dissemination is abstracted as continuous "goodput" flows:

  $$P^{\rm D}_i = \min\left( \sum_{j \in \mathcal{N}^{\rm out}_i} P^{\rm U}_j, P^{\rm D}_{i,\max} \right)$$

  $P^{\rm U}_i = R_i \frac{P^{\rm D}_i}{n^{\rm in}_i}$

  where $R_i$ is a repeatability factor, $n^{\rm out}_i$ and $n^{\rm in}_i$ are outgoing/incoming links per peer.

Simulations (1,200 peers, with variable sources and superpeers) show that maintaining $R \geq 0.9$ is critical for swarm stability, connection limits ($N \approx 8$) prevent upload dilution, and moderate superpeer deployment (1–2% of swarm) optimizes goodput and system robustness.

7. SDRTV-to-HDRTV Conversion Paradigm

A separate but prominent use of TV2TV is for SDRTV-to-HDRTV (Standard Dynamic Range TV to High Dynamic Range TV) up-conversion (Chen et al., 2023, Xu et al., 2023). The technical challenge is to recover wide color gamut and dynamic range from legacy SDRTV content, which entails:

• Modeling the forward SDR/HDR formation as tone mapping, fixed gamut transform, nonlinear OETF, and quantization, and learning an effective, invertible mapping.
• HDRTVNet++ (Chen et al., 2023): A divide-and-conquer generator architecture with three steps:
  1. Adaptive Global Color Mapping (pixel-wise, global-statistics–modulated cascade of $1\times1$ convolutions)
  2. Local Enhancement (U-Net encoder-decoder with skip connections and spatial feature transforms)
  3. GAN-based highlight refinement

The method achieves new SOTA on the HDRTV1K dataset (4K resolution) in PSNR (38.60 dB), SSIM (0.9745), and perceptual HDR-VDP3 (8.75), and can be implemented as a small LUT for hardware compatibility.

• DIDNet (Xu et al., 2023): Treats the problem as dual inverse restoration—simultaneous coding artifact removal and inverse tone mapping—via an architecture that incorporates spatio-temporal feature alignment, wavelet-attention frequency enhancement, and dual-modulation inverse tone mapping. It demonstrates superior PSNR, SSIM, and color-difference metrics over prior work.

Approach	Domain	Key Components
TV2TV (MoT) (Han et al., 4 Dec 2025)	Gen. Video Generation	Interleaved text & video
TV2TV (P2P overlay)(Biernacki, 2012)	Live IPTV	Goodput fluid model, superpeers
HDRTVNet++ (Chen et al., 2023)	SDR→HDR conversion	AGCM, LE, GAN, 4K support
DIDNet (Xu et al., 2023)	SDR→HDR (artifacts)	Restoration + tone mapping

8. Limitations and Prospects

• TV2TV (MoT): Performance is currently bottlenecked by the density/quality of interleaved captions (VLM-generated text generally less accurate and temporally dense than true ground-truth actions), and scaling context length and model size requires more sophisticated attention or sparsely-gated MoE architectures.

• P2P TV2TV overlays: Effectiveness depends on strong peer cooperation ($R$), careful selection of superpeer ratios, and realistic modeling beyond the idealized, lossless network abstraction.
• SDR→HDR TV2TV: Both HDRTVNet++ and DIDNet reveal that color-band preservation and artifact suppression are essential; the design of lightweight, real-time-compatible modules is likely critical for broadcast deployment.

TV2TV, across its modern incarnations, exemplifies the convergence of language, vision, and communications research for controllable, high-fidelity video synthesis, conversion, and distribution.