Removing Objects and Their Effects from Video

# Illustrative sketch (not from the paper)
import torch
from torch import nn

# 1. Highlight target objects in each frame (alpha‑blending with a fixed color)
def highlight_video(frames, masks, highlight_color=(1.0, 0.0, 0.0)):
    highlighted = []
    for img, mask in zip(frames, masks):
        # img: C×H×W, mask: 1×H×W (binary)
        color_tensor = torch.tensor(highlight_color).view(3,1,1)
        highlighted.append(img * (1 - mask) + color_tensor * mask)
    return torch.stack(highlighted)

# 2. Query a VLM (e.g., Qwen2.5‑VL‑3B‑Instruct) with a structured prompt
#    to obtain compact effect‑aware tokens (placeholder implementation)
def get_effect_tokens(highlighted_video, prompt):
    # In practice this calls the VLM API and extracts token embeddings
    batch, t, c, h, w = highlighted_video.shape
    return torch.randn(batch, 8, 768)  # token dim from paper's VLM backbone

# 3. DiT‑based Video Eraser conditioned on effect tokens
class VideoEraser(nn.Module):
    def __init__(self, token_dim=768):
        super().__init__()
        self.backbone = nn.Identity()  # placeholder for Wan2.2‑TI2V‑5B
        self.cond_proj = nn.Linear(token_dim, 512)

    def forward(self, video, masks, effect_tokens):
        # video: B×T×C×H×W, masks: B×T×1×H×W
        cond = self.cond_proj(effect_tokens).unsqueeze(1)  # B×1×512
        # simple conditioning by addition (illustrative)
        return video * (1 - masks) + cond.view(video.shape[0],1,1,1,1)

# 4. Motion‑aware mask union over a temporal window
def union_mask(masks, window=3):
    B, T, _, H, W = masks.shape
    union = torch.zeros_like(masks)
    for t in range(T):
        start = max(0, t - window // 2)
        end = min(T, t + window // 2 + 1)
        union[:, t] = torch.max(masks[:, start:end], dim=1).values
    return union

# 5. Motion‑consistency loss (latent difference after motion compensation)
def motion_consistency_loss(latent, flow):
    # latent: B×T×D, flow: B×T×2×H×W (placeholder)
    # simple L2 between adjacent frames after warping (illustrative)
    loss = 0.0
    for t in range(latent.shape[1] - 1):
        loss += ((latent[:, t] - latent[:, t+1]) ** 2).mean()
    return loss

# Example pipeline (illustrative, not runnable end‑to‑end)
frames, masks = torch.randn(1,5,3,256,256), torch.randint(0,2,(1,5,1,256,256)).float()
highlighted = highlight_video(frames, masks)
prompt = "Analyze object‑induced effects and return compact tokens."
effect_tokens = get_effect_tokens(highlighted, prompt)
eraser = VideoEraser()
unioned_masks = union_mask(masks)
output = eraser(frames, unioned_masks, effect_tokens)
# latent representation would be obtained from the backbone; here we mock it
latent = torch.randn(1,5,512)
flow = torch.randn(1,5,2,256,256)
loss = motion_consistency_loss(latent, flow)
print(loss.item())

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for PyTorch‑like ops

// 1. Alpha‑blend highlight color onto target masks
function highlightVideo(frames, masks, highlight = [1.0, 0.0, 0.0]) {
  const highlighted = [];
  for (let i = 0; i < frames.length; i++) {
    const img = frames[i]; // [C,H,W]
    const mask = masks[i]; // [1,H,W]
    const color = torch.tensor(highlight).reshape([3,1,1]);
    highlighted.push(img.mul(torch.sub(1, mask)).add(color.mul(mask)));
  }
  return torch.stack(highlighted); // [T,C,H,W]
}

// 2. Query VLM (e.g., Qwen2.5‑VL‑3B‑Instruct) – mock token extraction
function getEffectTokens(highlightedVideo, prompt) {
  const [batch, T, C, H, W] = highlightedVideo.shape;
  // Return random token tensor matching expected dim (8 tokens × 768)
  return torch.randn([batch, 8, 768]);
}

// 3. Simple DiT‑style eraser conditioned on tokens (placeholder backbone)
class VideoEraser {
  constructor(tokenDim = 768) {
    this.condProj = torch.nn.Linear(tokenDim, 512);
  }
  forward(video, masks, effectTokens) {
    const cond = this.condProj.forward(effectTokens).unsqueeze(1); // [B,1,512]
    // Broadcast and add as illustrative conditioning
    return video.mul(torch.sub(1, masks)).add(cond.view(video.shape[0],1,1,1,1));
  }
}

// 4. Motion‑aware union mask over a temporal window
function unionMask(masks, window = 3) {
  const [B, T, _, H, W] = masks.shape;
  const union = torch.zerosLike(masks);
  for (let t = 0; t < T; t++) {
    const start = Math.max(0, t - Math.floor(window/2));
    const end = Math.min(T, t + Math.floor(window/2) + 1);
    const slice = masks.slice(1, start, end); // [B,window,1,H,W]
    union.select(1, t).copy(torch.max(slice, 1).values);
  }
  return union;
}

// 5. Motion‑consistency loss (L2 between adjacent latent frames)
function motionConsistencyLoss(latent) {
  let loss = 0.0;
  for (let t = 0; t < latent.shape[1] - 1; t++) {
    const diff = torch.sub(latent.select(1, t), latent.select(1, t+1));
    loss += diff.pow(2).mean().item();
  }
  return loss;
}

// Mock data for illustration
const frames = torch.randn([1,5,3,256,256]);
const masks = torch.randint(0,2,[1,5,1,256,256]).float();
const highlighted = highlightVideo(frames, masks);
const prompt = "Analyze object‑induced effects and return compact tokens.";
const effectTokens = getEffectTokens(highlighted, prompt);
const eraser = new VideoEraser();
const unionedMasks = unionMask(masks);
const output = eraser.forward(frames, unionedMasks, effectTokens);
const latent = torch.randn([1,5,512]);
const loss = motionConsistencyLoss(latent);
console.log('Motion consistency loss:', loss);