PISCO: Precise Video Instance Insertion with Sparse Control

arXiv:2602.08277 · cs.CV, cs.AI · Submitted 2026-02-09 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "PISCO: Precise Video Instance Insertion with Sparse Control".

Jane: PISCO (Precise Video Instance Insertion with Sparse Control) is a video diffusion model designed to enable precise, controllable insertion of specific objects into existing footage under minimal user effort.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Let's talk about the title of PISCO: Precise Video Instance Insertion with Sparse Control. It immediately tells us that the main goal is precise insertion, but they’ve added sparse control, which is key to making it usable without overwhelming users with data.

Jane: Exactly, Tom; the authors are Xiangbo Gao and Renjie Li, and they are proposing a video diffusion model specifically for this task. The implication here is that we move away from broad generation toward highly targeted modifications of existing footage.

Lu: The paper points out that standard video diffusion models often struggle with these requirements because they aren't inherently designed for fine-grained control under sparse conditions, so PISCO is a direct response to those limitations.

Meng: I’m curious how they balance the need for high fidelity—like maintaining shadows and reflections—with the constraint of only using a few keyframes for guidance. That seems like a very tight engineering challenge to solve.

Lalam: It suggests that future AI systems won't just be about generating new things from scratch, but about intelligently editing and augmenting existing media in highly controlled ways. That capability is incredibly powerful for creative industries.

The paper's summary: Tom: Now, let's move into the actual summary of PISCO. Essentially, they describe a video diffusion framework that uses a multi-channel context adapter to ingest instance information like RGB, mask, depth, and an availability signal for sparse guidance.

Jane: What I’m focusing on is how this architecture handles the insertion process; they aim to propagate appearance and motion in a scene-consistent manner while accounting for things like shadows and reflections that need to be correct after insertion.

Lu: They are using an availability mask A = I, MA = A M, and DA I = A DI to control exactly when the instance information is available at any given time, which lets them tune the level of supervision they need.

Meng: That flexibility, going from a single keyframe input to dense per-frame supervision, suggests a very scalable system design that can adapt its complexity based on user needs.

Lalam: It’s about building a unified model that understands both the background scene and the inserted object simultaneously, which is a significant step forward for complex video tasks.

The paper's improvements: Tom: The paper details several specific mechanisms they introduced to tackle the distribution shift problems that sparse conditioning causes when using these pretrained diffusion models. They highlight Variable-Information Guidance, or VIG, and Distribution-Preserving Temporal Masking, or DPTM.

Jane: DPTM seems like a clever way to stabilize things temporally by decoupling information masking from distribution preservation through pixel-space interpolation followed by token-space masking in the latent space. That should help prevent flickering when frames are missing.

Lu: I also want to point out their geometry-aware conditioning, where they condition the model on two depth signals—the background depth and the instance depth—to make it reason about relative depth ordering for physically plausible compositing.

Meng: Those geometric inputs are crucial because simply placing an object in a frame isn't enough; you need to know if it’s behind another object or how its shadow should fall correctly on the surface below. That level of physical reasoning is what makes it robust.

Lalam: The combination of these training strategies, like amodal instance augmentation and relighting augmentation, really shows a deep commitment to ensuring the final output looks physically coherent under various lighting conditions.

Conclusion: Tom: So, to wrap up on PISCO: Precise Video Instance Insertion with Sparse Control, the authors show that their framework consistently outperforms inpainting and video editing baselines when using sparse control settings. They also demonstrated clear performance improvements as they added more control signals.

Jane: That’s a strong result; it validates their approach of balancing user effort with high fidelity, especially when you use sparse "First and Last" frame control for insertion. The paper concludes by showing that PISCO scales well as you provide more control signals.

Lu: From a theoretical perspective, the implication is that we can design diffusion models specifically to handle temporally sparse conditioning robustly without catastrophic failure in rendering or scene dynamics.

Meng: For practical engineering, it means we can build tools where users only need to specify anchor points, and the AI takes care of the complex temporal propagation and physical effects automatically.

Lalam: This work solidifies PISCO as a practical solution for professional-grade video editing, moving us closer to a future where high-fidelity AI assistance in filmmaking is accessible with minimal user input.

Xiangbo Gao, Renjie Li, Xinghao Chen, Yuheng Wu, Suofei Feng, Qing Yin, Zhengzhong Tu

Texas A&M University · KAIST University of Science and Technology Graduate School of Computer Science and Engineering, Stanford University

cs.CV, cs.AI

Submitted: 2026-02-09

Updated: 2026-09-30

Comments: Accepted at NeurIPS 2026

Project page: https://xiangbogaobarry.github.io/PISCO

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 91/100

The gist: PISCO (Precise Video Instance Insertion with Sparse Control) is a video diffusion model designed to enable precise, controllable insertion of specific objects into existing footage under minimal user

Key concepts

Sparse Keyframe Control
Instead of needing dense control over every frame, PISCO uses sparse keyframes to guide the insertion process. This means users only need to provide specific points in time where they want the object inserted, significantly reducing user input while still achieving precise placement.
Variable-Information Guidance (VIG)
To handle sparse guidance effectively, VIG is a dynamic dropout strategy used during training. It exposes the model to different levels of supervision, forcing it to learn how to maintain appearance fidelity when only sparse information is available.
Depth-aware Conditioning
The model uses two depth signals—the background depth and the instance depth—to understand the 3D layout of a scene. This allows the AI to correctly reason about relative depths, ensuring that inserted objects look physically plausible within the existing environment.

Terminology

Summary

PISCO (Precise Video Instance Insertion with Sparse Control) is a video diffusion model designed to enable precise, controllable insertion of specific objects into existing footage under minimal user effort. This capability is crucial for professional AI-assisted filmmaking, demanding the ability to insert an instance while maintaining scene integrity through precise spatial-temporal placement and physically consistent interactions like shadows and reflections. PISCO addresses the limitations of existing methods—such as dense inpainting masks or lacking fine-grained control in reference-guided editing—by allowing users to specify insertion points using sparse keyframes, thereby achieving high fidelity with low user interaction.

PISCO Architecture and Control Mechanism

PISCO is a video diffusion framework that builds upon the Wan video diffusion backbone and augments it with a multi-channel context adapter. This adapter ingests instance RGB, mask, depth, and an explicit availability signal indicating when instance guidance is provided, enabling flexible sparse keyframe control within a unified model. The core objective is to perform video instance insertion by propagating appearance, motion, and interaction in a scene-consistent manner.

The framework accommodates various levels of user input through an availability mask A = ⊙ I, MA = A ⊙ M, DA I = A ⊙ DI (Equation 1), where At ∈ [0, 1] indicates whether instance-side information is available at time t. This design allows for control ranging from a single keyframe to dense per-frame supervision. During training, the availability mask A is sampled from a density ratio γ ∈ [0, 1], which controls the expected fraction of available frames and their temporal placement.

Mechanisms for Sparse Control Robustness

To counteract the severe distribution shift induced by sparse conditioning in pretrained video diffusion models, PISCO introduces dedicated mechanisms:

  1. Variable-Information Guidance (VIG): A dynamic contextual dropout strategy that samples an availability mask A during training to expose the model to diverse supervision regimes, encouraging it to propagate instance information under sparse guidance while maintaining appearance fidelity and spatial alignment under dense guidance.

  2. Distribution-Preserving Temporal Masking (DPTM): This addresses temporal instability by decoupling distribution preservation from information masking through two steps: pixel-space temporal completion (nearest interpolation) followed by token-space masking in the latent space. This ensures that missing frames are filled by propagating the temporally nearest available instance frame forward and backward, preserving encoder input statistics.

Geometry-Aware and Appearance-Robust Training

PISCO incorporates three complementary strategies to ensure geometric plausibility and appearance robustness:

  1. Depth-aware conditioning: The model is conditioned on two depth signals—the background depth DV from V and the instance depth DI extracted from Vˆ—to enable the model to reason about relative depth ordering and produce physically plausible layer compositing.

  2. Amodal instance augmentation: To align training with inference requirements, PISCO introduces an amodal augmentation strategy where missing regions are reconstructed to generate a complete pseudo-amodal input condition, supervising the model with the original occluded video.

  3. Instance relighting augmentation: Training is augmented by synthesizing relighted versions of instances under randomly sampled background lighting conditions to improve automatic lighting adaptation and ensure coherent blending with the surrounding scene.

Evaluation and Performance

PISCO is evaluated using PISCO-Bench, a curated benchmark derived from BURST [16], assessing performance via reference-based metrics (FVD, LPIPS, PSNR, SSIM) and reference-free perceptual metrics (VBench [18]). Experiments demonstrate that PISCO consistently outperforms strong inpainting and video editing baselines under sparse-control settings. Furthermore, the results show clear and monotonic performance improvements as additional control signals are provided, validating its scalability with respect to control signal density. Qualitative results confirm that PISCO achieves superior visual fidelity and spatiotemporal alignment, particularly when utilizing sparse "First & Last" frame control.

Broader Applications

The instance-level conditioning and temporal propagation machinery of PISCO generalizes to a broader set of controllable video editing tasks, including:

: Background Change:

PISCO can perform background change by re-rendering the surrounding scene while preserving the foreground instance identity and motion.

: Instance Repositioning and Rescaling:

It supports adjusting the instance location and size while maintaining scene-consistent interactions, such as occlusions and shadows.

: Speed Change:

It realizes speed change by temporally subsampling instance-related conditions to induce faster or slower motion.

: Dynamics Simulation:

It enables dynamics simulation by providing partial instance-related conditions to create counterfactual trajectories for stress-testing downstream perception and planning systems.

Conclusion

PISCO is presented as a practical and scalable solution for professional-grade video editing, positioning it as a key step toward highly controllable, low-effort, AI-assisted filmmaking.

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed PISCO (Precise Video Instance Insertion with Sparse Control). Based on its architecture, training methodologies, and experimental results, here are specific improvements that can be made to AI systems and what those improved systems can achieve.

The core improvements stem from integrating the sparse control mechanism with robust temporal stability and physical consistency.

  1. Improve the robustness of video diffusion models under sparse conditioning by formally incorporating a Distribution-Preserving Temporal Masking (DPTM) module, specifically combining pixel-space nearest-frame interpolation with token-level masking aligned to compressed temporal resolutions.

  2. Enhance the flexibility of user input handling by implementing a Variable-Information Guidance (VIG) framework, which dynamically samples an availability mask during training to expose the model to diverse supervision regimes (ranging from single keyframe control to dense per-frame supervision).

  3. Increase geometric plausibility and scene consistency by integrating explicit depth-aware conditioning, using both background depth maps and instance depth maps derived from the original video, ensuring the model reasons about relative depth ordering and occlusion during insertion.

  4. Improve appearance fidelity by augmenting training with three complementary strategies: occlusion-aware completion augmentation (training on pseudo-amodal inputs), relighting augmentation (synthesizing instances under random lighting conditions using IC-Light), and amodal instance augmentation (forcing the model to learn compositing logic for occlusions).

The resulting improved AI system, built upon these enhancements, can perform the following specific capabilities:

  1. Perform high-fidelity, precise video instance insertion with minimal user effort.

  2. Insert a specific object into existing footage at an arbitrary spatial location and temporal position (specified via single keyframes or start/end keyframes) while maintaining physically consistent scene interactions (e.g., shadows, reflections, water ripples).

  3. Preserve the identity and dynamics of the original background scene perfectly after insertion, ensuring pre-existing motions and temporal patterns remain unchanged.

  4. Adapt inserted instances to the new scene's illumination and perspective realistically through learned relighting capabilities, avoiding lighting mismatch artifacts common in current methods.

  5. Scale controllability: The system can handle a wide spectrum of user input density—from extremely sparse control (first-frame only) to dense, high-fidelity control (first & last frame supervision)—without catastrophic failure in temporal stability or geometric coherence.

  6. Support broader instance-centric video editing tasks beyond simple insertion, including dynamic background change and instance repositioning/rescaling while maintaining scene consistency.

Sources

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