Video Connecting: Algorithms & Benchmarks

• Video connecting is a framework that unifies separate video segments using algorithmic strategies to achieve smooth, semantically consistent transitions.
• It employs innovative models such as interpolation-based synthesis and multimodal fusion to align temporal, spatial, and cross-modal cues across video segments.
• Evaluation metrics like VQS, SECS, and TSS quantitatively assess perceptual realism and continuity, guiding improvements in video connecting systems.

Video connecting refers broadly to the algorithmic, architectural, and evaluative frameworks enabling the seamless linkage, fusion, or alignment of separate video segments or modalities into unified, coherent outputs. This encompasses tasks such as synthesizing intermediate content between start and end clips, aligning multiple videos temporally or semantically, bridging discontinuous visual and audio streams, fusing multimodal cues, and ensuring perceptual or physical consistency across transitions. Recent advances have formalized rigorous benchmarks, devised specialized models, and established new evaluation metrics for video connecting in both generative and analytical contexts.

1. Task Formalization and Benchmarking

The modern task of video connecting is defined as generating a temporally and visually coherent sequence $V$ that links a designated start clip $V_S$ and end clip $V_E$. The generated sequence $V$ must satisfy:

• Exact correspondence to $V_S$ for the first $N_S$ frames and to $V_E$ for the last $N_E$ frames.
• Smooth spatial-temporal continuity and preservation of semantic content, despite possible scene variation (lighting, objects, etc.) between $V_S$ and $V_E$. Unlike frame interpolation (which only bridges two frames), video connecting operates on extended clips (e.g., 48–96 frames each) and may involve scene-level as well as fine-grained transitions (Yin et al., 27 Jan 2026).

The VC-Bench benchmark provides the first standardized testbed for this task, comprising 1,579 high-quality natural videos extracted from diverse public sources. VC-Bench enforces strict start-end constraints and covers 15 main categories and 72 subcategories to represent realistic transitions, including cross-scene and within-scene linking (Yin et al., 27 Jan 2026).

2. Evaluation Metrics and Quantitative Analysis

Traditional metrics emphasizing only per-frame visual realism are insufficient for assessing video connecting. VC-Bench introduces three principal evaluation axes:

Metric	Assessed Property	Key Computation/Measure
VQS (Video Quality)	Perceptual realism, subject/bg consistency	DINO/CLIP similarity, flicker, LAION/MUSIQ scores
SECS (Start-End Cons.)	Pixel & motion consistency at boundaries	SSIM at segment boundaries, error in optical flow
TSS (Transition Smooth.)	Local and global temporal continuity	DTW-based drift, VGG perceptual jumps

The overall score is the arithmetic mean:

$\mathrm{Score} = \frac{VQS + SECS + TSS}{3}$

• VQS evaluates both spatial (texture, image quality) and semantic stability (subject and background preservation, flicker reduction).
• SECS fuses pixel-level and flow-based agreement between generated transitions and the anchor segments.
• TSS measures both global alignment (using DTW on SSIM) and local perceptual jumps via deep feature $L^1$ distances (Yin et al., 27 Jan 2026).

Empirically, state-of-the-art generation models (e.g., Wan2.1, CogVideoX) achieve 0.82–0.96 per-component scores on VC-Bench within segments sharing similar scenes, but coherence degrades ($\sim$0.03–0.04 score loss) for cross-scene connections, revealing significant open challenges (Yin et al., 27 Jan 2026).

3. Model Architectures and Connecting Strategies

3.1 Interpolation-Based Synthesis

Video Motion Graphs (Liu et al., 26 Mar 2025) instantiate connection by mapping video frames as graph nodes, linking those with minimal 2D/3D pose discontinuity. A global search then produces a plausible sequence that matches desired task/semantic cues and minimizes motion gaps. Explicit frame interpolation between hard graph boundaries is performed via HMInterp, a dual-branch model combining:

• A Motion Diffusion Model (MDM) for non-linear skeleton trajectory interpolation,
• A UNet-based Video Frame Interpolation branch for photorealistic RGB synthesis (using CLIP and latent conditionings). Condition-progressive training is fundamental to maintain appearance fidelity and inject accurate motion control retrospectively, preserving both global identity and smooth transitions (Liu et al., 26 Mar 2025).

3.2 Multimodal and Cross-Modal Fusion

Zero-shot AVS (Audio-Visual Segmentation) exemplifies multimodal video connecting. Here, pretrained audio, vision, and text models are “connected” using a late-fusion text prompt mechanism:

• Audio encoders (e.g., CLAP, BEATs) classify or caption the audio signal.
• Prompts are constructed for RIS (Referring Image Segmentation) models, e.g., “a photo of a {class}.”
• Cross-modal strategies use image captioners and noun extraction to gate possible audio matches, thereby improving segmentation alignment. Careful engineering of these “connectors” (prompts) yields significant gains in object-level audiovisual coherence, achieving mIoU scores 25–30% higher than previous zero-shot/unsupervised AVS systems (Lee et al., 6 Jun 2025).

4. Temporal Alignment and Synchronization in Multiple Videos

Synchronizing multiple videos (real or generative) necessitates a robust temporal correspondence, especially in presence of nonlinear misalignments and diverse content. Temporal Prototype Learning (TPL) (Naaman et al., 15 Oct 2025) constructs an optimal 1D sequence of K semantic prototypes in embedding space. It then assigns each video a monotonic, non-linear warping $a_{(v)}$ onto this prototype axis using dynamic programming.

The clustering and temporal regularization loss:

$$L_\text{total} = \sum_{v,t} \|f_{(v)}(t) - p_{a_{(v)}(t)}\|^2 + \lambda \sum_{v,t} (a_{(v)}(t+1) - a_{(v)}(t) - 1)^2$$

enforces both semantic similarity and timeline regularity. TPL significantly outperforms pairwise DTW matching in frame retrieval accuracy (+21.8%) and alignment efficiency (3.8$\times$ faster), and is robust to style or background variation, making it suitable for Gen-AI video alignment (Naaman et al., 15 Oct 2025).

5. Applications: Real-Time, Telepresence, and Multimodal Narratives

DataTV and Live Multisource Fusion

Live video production systems such as DataTV (Zhao et al., 2022) embody real-time video connecting via low-latency ingest, GPU-accelerated scene composition, per-source queuing, and adaptive encoding. Their architecture harmonizes multiple live media streams (video, desktop captures, audio) into a single composited output, with bounded ($\sim$178ms) end-to-end latency and dynamic quality-of-service adaptation. Core compositing and synchronization ensure frame-accurate mapping of temporally disparate sources, essential for streaming, live broadcast, and interactive data storytelling.

Telepresence and Interactive Video Manipulation

Telepresence systems (Jia et al., 2015) enable remote operation by “connecting” video capture, network transmission, and direct graphical interaction (touch overlays). Unified live-video windows minimize situational disorientation, and supervisory stepwise control links user input with system feedback via round-trip video streaming. The synchronization of touch, actuation, and video stream update is integral to closed-loop operation, although the referenced architecture omits explicit detail on the network/codec layer.

Narrative and Semantic Video Grounding

Video Localized Narratives (Voigtlaender et al., 2023) extend connection to vision-language linking, annotating videos with densely grounded free-form natural language. Narratives are temporally and spatially linked to keyframes via mouse trace alignment, enabling new Video Narrative Grounding and spatio-textual VideoQA tasks.

SignCLIP (Jiang et al., 2024) and related contrastive approaches further connect modalities at the embedding level, projecting sign-language video and text into common spaces for retrieval or recognition.

6. Cross-Modal, Self-supervised, and Geometric Connections

GLNet (Chen et al., 2019) demonstrates physical (geometric and photometric) video connecting by jointly predicting depth, optical flow, and camera (intrinsic and extrinsic) parameters from monocular video. All predictions are tightly coupled via geometric constraints (epipolar, photometric, multi-view consistency), yielding bundle-adjustment–like optimization in a neural, self-supervised regime. The connection here is not only algorithmic but also physically priors-driven.

DOT (Dense Optical Tracking) (Moing et al., 2023) exemplifies efficient, dense spatial-temporal video connecting by propagating sparse, reliable point tracks throughout the video, then inferring the full dense flow and occlusion mask with a learnable RAFT-variant network. This achieves near real-time, per-pixel resolution tracking “connecting the dots” with explicit, learnable handling of occlusions.

7. Open Challenges and Research Directions

Despite advances, the video connecting task presents persistent challenges:

• Intermediate segment generation remains difficult in cross-scene scenarios, with perceptual and temporal artifacts still prominent in benchmarks (Yin et al., 27 Jan 2026).
• Real-time systems must balance latency, synchronization, and quality, particularly in constrained or heterogeneous environments (Zhao et al., 2022Jia et al., 2015).
• Cross-modal representation alignment (audio, vision, text) depends heavily on robust prompt engineering and embedding space coherence (Lee et al., 6 Jun 2025Jiang et al., 2024).
• Structural connections (e.g. flow, depth, camera) require careful multi-task constraint design for self-supervision (Chen et al., 2019).
• Comprehensive benchmarks such as VC-Bench (Yin et al., 27 Jan 2026) are now spurring the development and objective evaluation of new methods across diverse real-world scenarios.

In summary, video connecting is a multi-faceted area that integrates video synthesis, alignment, fusion, and interaction, underpinned by both generative and analytic models, and formalized by emerging benchmarks and multi-dimensional evaluation protocols. It continues to evolve as a central challenge at the intersection of computer vision, graphics, and multimodal machine learning.