VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing

• Frame-wise text-driven diffusion can flicker, while spatiotemporal-attention methods struggle with long-term dependencies and atlas editors are costly and lack precise spatial control.The attention-based approaches also demand significant memory resources.Source: §1 · p. 2
• VidEdit uses a pretrained text-driven image diffusion model to edit layered neural atlases, preserving temporal coherence when edits map back to the original frames.This training-free approach edits a full video in about one minute.Source: §1 · p. 2
• Panoptic segmentation identifies regions of interest and HED edges guide diffusion, enabling fine-grained spatial control while preserving atlas structure and geometry.The method uses a crop around the area of interest and a non-invertible noising process.Source: §1 · pp. 2–3
• On DAVIS, VidEdit outperforms baselines in semantic matching, original-content preservation, and temporal consistency, while also producing diverse edits compatible with one prompt.The paper also reports efficient processing, with a full video edited in about one minute.Source: §1 · p. 3

• Text-to-image diffusion supports text-conditioned editing, including masked local edits and attention-based region constraints; some image-editing methods refine results through optimization.Latent diffusion models move the diffusion process into an autoencoder’s latent space to improve training efficiency, memory, and runtime.Source: §2 · p. 3
• Video-generation and editing methods use spatiotemporal attention, but reported approaches still exhibit flicker or struggle with reliable long-term coherence (Wu et al., 2022; Qi et al., 2023; Ceylan et al., 2023; Wang et al., 2023; Liu et al., 2023).Tune-A-Video overfits a video to a text query, while other approaches transfer pretrained text-to-image knowledge through spatiotemporal attention.Source: §2 · p. 3
• Neural Layered Atlases provide temporally consistent 2D video representations; Text2Live optimizes an edit layer per prompt, whereas VidEdit uses pretrained diffusion to avoid that optimization and enable quick inference.Text2Live is identified as an atlas-based editing method (Bar-Tal et al., 2022).Source: §2 · p. 3

3 VidEdit Framework

• The framework decomposes video into neural layered atlases, edits a selected atlas with diffusion, then samples frame edit layers and composites them over original frames.The atlas representation is used to provide global spatial and temporal coherence.Source: Figure 2 · p. 4, §3 · p. 4

3.1 Zero-shot Atlas-based video editing

• Neural Layered Atlases map each video pixel to foreground and background atlas coordinates and predict foreground opacity, enabling layered color reconstruction and projection of edited atlas values to frames.The atlases are trained self-supervised and disentangle video layers.Source: §3.1 · p. 4
• Pretrained conditional latent diffusion turns video editing into a training-free atlas-image editing task, unlike methods that train a generative model for each text query.The approach is described as adaptable and yielding competitive performance.Source: §3.1 · pp. 4–5

3.2 Semantic Atlas Editing with VidEdit

• Panoptic segmentation on atlas grids identifies a target region and produces a mask, with alpha-based white-background mixing helping the segmenter distinguish object from background.A bounding box around the identified region narrows the patch for mask generation.Source: §3.2 · pp. 5–6
• HED edges provide structural guidance alongside the target prompt during diffusion, while the mask keeps the generation focused on the selected object region.The method crops the atlas patch around the target bounding box, encodes it as a latent, and applies a 1,000-step noising process.Source: §3.2 · p. 6
• After editing, the region outside the mask is replaced from the input atlas, and the edited atlas is sampled back into frame layers for compositing with original frames.This produces an edited video described as spatially and temporally consistent.Source: §3.2 · p. 6

4.1 Experimental setup

• The DAVIS evaluation uses 20–70-frame videos resized to 768 × 432, four manually designed prompts per video, and compares against frame-wise and video-editing baselines.VidEdit uses Mask2former segmentation and HED conditioning with ControlNet Stable Diffusion; a 70-frame video takes about one minute to reconstruct after patch editing.Source: §4.1 · p. 7

4.2 State-of-the-art comparison

• VidEdit outperforms baselines on semantic and similarity metrics, achieves the best directional similarity, and exceeds all methods in CFrame while matching Text2Live's temporal consistency.It also outperforms all baselines in LPIPS and HaarPSI and is similar to Text2Live on PSNR.Source: §4.2 · pp. 8–9, Table 1 · p. 9
• VidEdit reaches strong local frame accuracy and improves out-of-interest LPIPS over baselines, including Text2Live (3 vs 8).Figure 4 compares local frame accuracy within a ground-truth mask with LPIPS outside it.Source: §4.2 · p. 9, Figure 4 · p. 9
• For interactive editing, VidEdit edits a 70-frame video approximately 30 times faster than Text2Live, and its time-efficiency advantage over other baselines increases as the number of frames grows.Source: §4.2 · p. 9
• Qualitative comparisons show finer, more realistic localized edits from VidEdit, while Text2Live can miss detailed textures and Tune-a-Video and Pix2Video alter untargeted regions.Despite high frame-consistency scores, Tune-a-Video and Pix2Video show noticeable flickering; SDEdit struggles to balance faithful editing with content preservation.Source: §4.2 · pp. 9–10, Figure 6 · p. 10

4.3 Model Analysis

• Removing conditional controls causes edits at random atlas locations with shapes mismatched to target objects; edge-only control improves metrics slightly, while mask-only edits can be semantically incoherent.The ablation compares the full mask-and-HED pipeline with disabled or partial controls.Source: §4.3 · p. 11, Table 2 · p. 11
• HED strengths below 0.4 provide insufficient guidance, while strengths above 1.2 hinder faithful edits; local LPIPS falls as conditioning increases, with diminishing gains beyond 1.The analysis compares local LPIPS and local CLIP scores within DAVIS ground-truth masks.Source: §4.3 · p. 11, Figure 7 · p. 11
• Repeated edits from one video and prompt produce more diverse samples with VidEdit than Text2Live, whose training randomness tends toward a unique solution.Figure 8 compares four edits per video using the same text query.Source: §4.3 · p. 12, Figure 8 · p. 12

5 Conclusion & Discussion

• VidEdit is a lightweight algorithm for zero-shot semantic video editing based on latent diffusion models.Source: §5 · p. 12
• VidEdit preserves more appearance information from the input than other diffusion-based methods, producing lighter edits.Source: §5 · p. 12
• The method’s atlas-based MLP mapping networks lose capacity on complex videos with rapid movement and on very long-term videos.Because VidEdit relies on atlas quality, strengthening and robustifying neural layered atlas construction could expand the scope of possible edits.Source: §5 · p. 12

A Ablation Visualization

• Using both conditional controls produces high-quality results in the ablation visualization.Source: A Ablation Visualization · p. 15, Figure 9 · p. 15
• With both controls disabled, edits occur at random atlas locations and produce uninterpretable visual outcomes.Source: A Ablation Visualization · p. 15
• Edge conditioning alone leads the model to locally match inner and outer edges, while mask-only editing targets the correct locations but can look semantically absurd after mapping back to image space.Source: A Ablation Visualization · p. 15

B.1 VidEdit samples

• Figure 10 shows additional VidEdit sample results.Source: Figure 10 · p. 16

B.2 Baselines Comparison

• VidEdit renders more realistic, higher-quality textures than the compared methods while preserving original content outside regions of interest.Source: §B.2 · p. 17, Figure 11 · p. 17
• In the flamingo example, VidEdit renders subtle feather-like grooves and a fine light effect, while Text2Live struggles to produce a detailed plastic appearance.Source: §B.2 · p. 17
• For the wooden boat, VidEdit looks more natural and less tarnished than the generated boat from the comparison methods.Source: §B.2 · p. 17
• Tune-a-Video and Pix2Video produce unconvincing edits and substantially alter the original content.Source: §B.2 · p. 17

C Blending Effect

• Alpha-guided blending avoids mask errors that can arise from low contrast and duplicated object representations in the atlas.Using only foreground RGB can yield partially detected objects or masks at incorrect locations; the alpha channel identifies pixels with relevant visible information.Source: C Blending Effect · p. 18
• The atlas RGB channels are blended with a fully white image according to the atlas alpha values.The passage identifies ARGB as atlas RGB channels, I as a fully white image, and α as atlas opacity values.Source: C Blending Effect · p. 18
• Figure 12 presents the blending effect used in the editing pipeline.Source: Figure 12 · p. 18, C Blending Effect · p. 18

D Atlas Construction

• The atlas construction takes a video and rough object masks and produces atlases, pixel-to-atlas mappings, and per-pixel opacity values.It discretizes the atlases to a 1000 × 1000 image grid for editing.Source: D Atlas Construction · p. 18
• Mapping networks map each video pixel location (x, y, t) to a 2D coordinate in its atlas.Source: D Atlas Construction · p. 18
• A shared atlas network outputs RGB color, while foreground and background mappings point into separate predefined quadrants and use positional encoding.Separate foreground and background atlas networks are not required in this setup.Source: D Atlas Construction · p. 18
• An alpha network outputs atlas opacity, bootstrapped with rough object masks from a pretrained segmentor.Source: D Atlas Construction · p. 18
• Reconstructed video-pixel RGB is estimated by alpha-blending the corresponding atlas colors.Source: D Atlas Construction · p. 19
• Training combines reconstruction with regularization for meaningful atlases, including local rigidity, cross-frame correspondence consistency, and sparsity.Consistency uses optical-flow pixel correspondences to map consecutive-frame pixels to the same atlas point; sparsity encourages recovering only the minimal content needed to reconstruct the video.Source: D Atlas Construction · p. 19